Market Transformation: Final Report

SGIP Market Transformation:

Final Report

Submitted to: PG&E and the SGIP

Working Group

Prepared by:

330 Madson Place

Davis, CA 95618

November, 2015

TABLE OF CONTENTS | i

TABLE OF CONTENTS

TABLE OF CONTENTS ............................................................................................................................................... i

LIST OF FIGURES ..................................................................................................................................................... v

LIST OF TABLES .................................................................................................................................................... vii

GLOSSARY ............................................................................................................................................................. ix

1 EXECUTIVE SUMMARY ................................................................................................................................. 1-1

1.1 Purpose of This Report ............................................................................................................................. 1-1

1.2 Conclusions ............................................................................................................................................... 1-2

1.3 Recommendations .................................................................................................................................... 1-5

2 SUMMARY OF RESULTS ................................................................................................................................ 2-1

2.1 Extent to which SGIP is Transforming California’s DG and Energy Storage Markets ................................ 2-1

Establishing SGIP Program Theory and Logic 2-2

Assessing SGIP’s Influence 2-3

2.2 Characterizing California’s DG and Energy Storage Markets .................................................................... 2-4

California’s Combined Heat and Power (CHP) Market ............................................................................. 2-5

California’s Wind Market .......................................................................................................................... 2-7

California’s Biogas Market ........................................................................................................................ 2-8

California’s Advanced Energy Storage Market ....................................................................................... 2-11

2.3 Overcoming Market Barriers .................................................................................................................. 2-14

Key Takeaways from Host Customer Surveys ......................................................................................... 2-14

Key Takeaways from Manufacturer and Project Developer Interviews 2-14

Overall PA/CPUC Perspective and Key Takeaways 2-15

2.4 Lessons Learned 2-16

3 INTRODUCTION ............................................................................................................................................ 3-1

3.1 Purpose and Objectives of this Study ....................................................................................................... 3-1

Overall Purpose of this Study ................................................................................................................... 3-1

3.2 Scope ........................................................................................................................................................ 3-2

Focus on SGIP but Comparisons to Other DG Incentive Programs ........................................................... 3-2

3.3 Defining Distributed Energy Resources within the Context of the SGIP ................................................... 3-3

3.4 California’s Programs for Distributed Energy Resources .......................................................................... 3-3

3.5 Overview of California’s Distributed Energy Resources ........................................................................... 3-4

3.6 Overview of SGIP ...................................................................................................................................... 3-5

3.7 Defining Market Transformation .............................................................................................................. 3-6

Market Transformation versus Resource Acquisition............................................................................... 3-7

Complementary Nature of Program Drivers, Policies and Market Players in Market Transformation .... 3-7

3.8 Report Organization.................................................................................................................................. 3-7

4 APPROACH ................................................................................................................................................... 4-1

4.1 Program Theory and Logic Models ........................................................................................................... 4-2

4.2 Characterizing California’s DG and Energy Storage Markets .................................................................... 4-5

Technical, Economic, and Market Potentials ............................................................................................ 4-5

SGIP Market Transformation Report

TABLE OF CONTENTS | ii

DG and Energy Storage Market Dynamics and Major Market Players ..................................................... 4-9

4.3 Collecting Information on Market Impacts and Market Players ............................................................ 4-10

Participant and Nonparticipant Surveys ................................................................................................. 4-11

Manufacturer Surveys ............................................................................................................................ 4-11

Project Developer Surveys ...................................................................................................................... 4-12

Stakeholder Surveys ............................................................................................................................... 4-13

PA Surveys .............................................................................................................................................. 4-13

4.4 Evaluating SGIP Influences ...................................................................................................................... 4-15

4.5 Assessing Market Trends Toward Self-Sufficiency .................................................................................. 4-15

5 PROGRAM LOGIC AND THEORY ................................................................................................................... 5-1

5.1 Results....................................................................................................................................................... 5-1

Defining Transformed Markets ................................................................................................................. 5-3

Role of the SGIP in DG and Energy Storage Market Transformation ........................................................ 5-5

5.2 Information Collected on Barriers and Opportunities .............................................................................. 5-6

Program Associated Staff Interviews ........................................................................................................ 5-6

Host Customer Interviews ...................................................................................................................... 5-11

Key Takeaways from Host Customer Surveys ......................................................................................... 5-17

Manufacturer and Developer Interviews ............................................................................................... 5-17

Key Takeaways from Manufacturer and Project Developer Interviews ................................................. 5-21

5.3 Barriers Facing SGIP Technologies .......................................................................................................... 5-21

5.4 Perceived Drivers to Growth in SGIP Technologies ................................................................................ 5-23

5.5 Expected Outcomes ................................................................................................................................ 5-23

6 CHARACTERIZING CALIFORNIA’S DG AND ENERGY STORAGE MARKETS ....................................................... 6-1

6.1 California’s Combined Heat and Power (CHP) Market ............................................................................. 6-1

Overall Approach ...................................................................................................................................... 6-2

Estimated CHP Technical Potential ........................................................................................................... 6-3

CHP Economic Potential .......................................................................................................................... 6-10

Potentials and Observed Growth in California’s CHP Market ................................................................. 6-12

CHP Market Potential ............................................................................................................................. 6-14

6.2 California’s Wind Market ........................................................................................................................ 6-17

Overall Approach .................................................................................................................................... 6-17

Wind Technical Potential Results ............................................................................................................ 6-18

Wind Economic Potential Results ........................................................................................................... 6-27

Wind Market Potential ........................................................................................................................... 6-29

6.3 California’s Biogas Power Generation Market ........................................................................................ 6-32


Landfill Gas Results ................................................................................................................................. 6-34

Dairy Biogas Results ................................................................................................................................ 6-37

Wastewater Treatment Plants Biogas Results ........................................................................................ 6-40

Summary of California’s Biogas Potentials ............................................................................................. 6-42

6.4 California’s Advanced Energy Storage Market ....................................................................................... 6-44


TABLE OF CONTENTS | iii


Estimated AES Technical Potential .......................................................................................................... 6-46

Estimated AES Economic Potential ......................................................................................................... 6-47

Estimated AES Market Growth and Potential ......................................................................................... 6-50

6.5 Historical Trends in DG and Energy Storage Growth in California .......................................................... 6-52

7 SGIP’S INFLUENCE ON DG MARKET TRANSFORMATION ............................................................................... 7-1

7.1 Assessing Influence through Indicators .................................................................................................... 7-2

CHP Programs, Policies, and Accomplishments ........................................................................................ 7-2

CHP in California: Programs and Policies .................................................................................................. 7-3

CHP in Connecticut: Programs and Policies .............................................................................................. 7-4

CHP in Illinois: Programs and Policies ....................................................................................................... 7-5

CHP in Massachusetts: Programs and Policies .......................................................................................... 7-7

CHP in Michigan: Programs and Policies ................................................................................................... 7-8

CHP in New Jersey: Programs and Policies ............................................................................................... 7-8

CHP in New York: Programs and Policies .................................................................................................. 7-9

CHP in Pennsylvania: Programs and Policies .......................................................................................... 7-11

CHP in Wisconsin: Programs and Policies ............................................................................................... 7-12

Wind Programs, Policies, and Accomplishments .................................................................................... 7-13

Wind in California: Programs and Policies .............................................................................................. 7-14

Wind in Illinois: Programs and Policies ................................................................................................... 7-16

Wind in Massachusetts and Programs and Policies ................................................................................ 7-17

Wind in Michigan: Programs and Policies ............................................................................................... 7-18

Wind in New Jersey: Programs and Policies ........................................................................................... 7-19

Wind in New York: Programs and Policies .............................................................................................. 7-20

Wind in Pennsylvania: Programs and Policies ........................................................................................ 7-21

Wind in Wisconsin: Programs and Policies ............................................................................................. 7-22

Biogas Programs, Policies, and Energy Accomplishments ...................................................................... 7-23

Biogas in California: Programs and Policies ............................................................................................ 7-24

Biogas in Connecticut: Programs and Policies ........................................................................................ 7-26

Biogas in Illinois: Programs and Policies ................................................................................................. 7-27

Biogas in Massachusetts: Programs and Policies .................................................................................... 7-28

Biogas in Michigan: Programs and Policies ............................................................................................. 7-30

Biogas in New Jersey: Programs and Policies ......................................................................................... 7-31

Biogas in New York: Programs and Policies ............................................................................................ 7-32

Biogas in Pennsylvania: Programs and Policies ....................................................................................... 7-33

Biogas in Wisconsin: Programs and Policies ........................................................................................... 7-35

7.2 Statistical Analysis of CHP, Wind, and Biogas Capacity .......................................................................... 7-36

Development of Independent Variables ................................................................................................. 7-37

Model Specifications ............................................................................................................................... 7-41

CHP Results ............................................................................................................................................. 7-42

Result for Wind and Biogas ..................................................................................................................... 7-45


TABLE OF CONTENTS | iv

7.3 Comparisons of SGIP to other DG and Energy Storage Programs – Program Administrator Surveys in the Northeast ........................................................................................................................ 7-46

DG and Energy Storage Program Goals .................................................................................................. 7-46

CHP Technology Drivers .......................................................................................................................... 7-47

CHP Technology Barriers ......................................................................................................................... 7-47

Impact of Regulations and Policies on DG and Energy Storage Adoption .............................................. 7-48

Best Practices in the Northeast:.............................................................................................................. 7-48

APPENDIX A MARKET TRANSFORMATION SURVEY FORMS ................................................................................ A-1

A.1 SGIP Host Customer Survey ..................................................................................................................... A-2

A.2 SGIP Program Administrator Survey ...................................................................................................... A-14

A.3 SGIP Manufacturer Survey ..................................................................................................................... A-35

A.4 SGIP Installer Survey .............................................................................................................................. A-46

A.5 SGIP Combined Manufacturer + Installer Survey .................................................................................. A-61

APPENDIX B HOST CUSTOMER SURVEY RESULTS ................................................................................................ B-1

B.1 Site Weighted Survey Results .................................................................................................................. B-1

SGIP Program Experience ........................................................................................................................ B-1

Technology Evaluation ............................................................................................................................. B-3

Priority ..................................................................................................................................................... B-8

B.2 Capacity Weighted Survey Results......................................................................................................... B-11

APPENDIX C POLICIES IMPACTING DG AND STORAGE ......................................................................................... C-1

APPENDIX D SITEPRO BUILDING TYPE TO NAICS CODE MAPPING ....................................................................... D-1

APPENDIX E CHP POTENTIAL ANALYSIS ............................................................................................................... E-1

E.1 Overview ................................................................................................................................................... E-1

E.2 Market Segment Host Buildings for CHP Systems .................................................................................... E-2

E.3 Building Electrical and Thermal Loads ...................................................................................................... E-5

E.4 CHP Systems Parameters .......................................................................................................................... E-5

E.5 CHP System Sizing ..................................................................................................................................... E-7


LIST OF FIGURES | v

LIST OF FIGURES

Figure 2 1: Preliminary SGIP Program Theory and Logic Diagram .............................................................................. 2-2

Figure 2 2: Growth in Cumulative CHP Capacity toward 2024 Market Potential ....................................................... 2-6

Figure 2 3: Wind Market Potential at 20% Average Annual Growth Rate .................................................................. 2-8

Figure 2 4: California’s Biogas Market Growth Forecast .......................................................................................... 2-11

Figure 2 5: AES Market Growth Forecast .................................................................................................................. 2-13

Figure 4 1: Linkages between DG Market Growth in California, Drivers and Barriers................................................ 4-2

Figure 4 2: Simplified Program Logic Layout .............................................................................................................. 4-4

Figure 4 3: Different Definitions of Potential.............................................................................................................. 4-6

Figure 5 1: Preliminary SGIP Program Theory and Logic Diagram .............................................................................. 5-2

Figure 5 2: Customer Satisfaction with Installed Technology ................................................................................... 5-12

Figure 5 3: Technology Recommendation to Friends and Associates ...................................................................... 5-13

Figure 5 4: Host Customer Considerations for Installing Technologies .................................................................... 5-14

Figure 5 5: Installation of the Technology in the Absence of SGIP Incentives .......................................................... 5-15

Figure 5 6: Satisfaction with Different Aspects of SGIP ............................................................................................ 5-16

Figure 6 1: How CHP Displaces Host Site Electrical and Thermal Loads ..................................................................... 6-4

Figure 6 2: Relationship of Food Manufacturing and Processing Segments to Other Market Segments .................. 6-5

Figure 6 3: 2014 Technical Potential Percentages by Segment and IOU .................................................................... 6-7

Figure 6 4: Total Resource Cost Test Benefit/Cost Ratios for Select Proxy CHP Systems ......................................... 6-11

Figure 6 5: Cumulative CHP Installed Capacity in CA for Select Prime Mover Technologies (1998-2014) ............... 6-12

Figure 6 6: Annual Capacity Additions in CA for Select Prime Mover Technologies................................................. 6-13

Figure 6 7: Growth in Cumulative CHP Capacity toward 2024 Market Potential ..................................................... 6-15

Figure 6 8: Growth in Cumulative CHP Capacity and Annual Capacity Additions ..................................................... 6-16

Figure 6 9: Distribution of Wind Speeds by County (50 Meters) .............................................................................. 6-22

Figure 6 10: Counties Representing 90% of Wind Technical Potential ..................................................................... 6-23

Figure 6 11: Distribution of Wind Class Speeds in Counties with IOU Overlay (50 Meters) ..................................... 6-26

Figure 6 12: Wind Economic Potential by Nominal Capacity and Year..................................................................... 6-27

Figure 6 13: Amount of Wind Energy Installed under the SGIP as of 2014 .............................................................. 6-29

Figure 6 14: Wind Market Potential at 10% Average Annual Growth Rate .............................................................. 6-30



Figure 6 17: Ranking of Landfill Gas Technical Potential by County ......................................................................... 6-35

Figure 6 18: TRC Benefit-to-Cost Ratios for Selected Landfill Biogas Generation Technologies .............................. 6-37

Figure 6 19: Counties Representing 90% of Dairy Biogas Technical Potential.......................................................... 6-39

Figure 6 20: TRC Benefit-to-Cost Ratios for Selected Dairy Biogas Generation Technologies ................................. 6-40

Figure 6 21: Ranking of Wastewater Treatment Plants Biogas Potential by County ................................................ 6-42

Figure 6 22: Total Technical, Economic, and Market Potential Growth 2014-2024 (kW)

with SGIP Biogas Projects ......................................................................................................................................... 6-43

Figure 6 23: Biogas Generation Market Growth Forecast ........................................................................................ 6-44


LIST OF FIGURES | vi

Figure 6 24: TRC Benefit-to-Cost Ratios for Residential AES .................................................................................... 6-48

Figure 6 25: TRC Benefit-to-Cost Ratios for Nonresidential AES .............................................................................. 6-49

Figure 6 26: AES Market Growth Forecast ................................................................................................................ 6-51

Figure 6 27: Forecasted Cumulative and Annual Year over Year Growth Rates ....................................................... 6-52

Figure 6 28: Trends in DG and Energy Storage Growth in SGIP and Associated Policies .......................................... 6-53

Figure 7 1: State Level CHP Accomplishments from 1999 to 2014 ............................................................................. 7-3

Figure 7 2: CHP California: Programs and Policies ...................................................................................................... 7-4

Figure 7 3: CHP in Connecticut: Programs and Policies .............................................................................................. 7-5

Figure 7 4: CHP in Illinois: Programs and Policies ....................................................................................................... 7-6

Figure 7 5: CHP in Massachusetts: Programs and Policies.......................................................................................... 7-7

Figure 7 6: CHP in Michigan: Programs and Policies ................................................................................................... 7-8

Figure 7 7: CHP in New Jersey: Programs and Policies ............................................................................................... 7-9

Figure 7 8: CHP in New York: Programs and Policies ................................................................................................ 7-11

Figure 7 9: CHP in Pennsylvania: Programs and Polices ........................................................................................... 7-12

Figure 7 10: CHP in Wisconsin: Programs and Policies ............................................................................................. 7-13

Figure 7 11: Wind Capacity by State ......................................................................................................................... 7-14

Figure 7 12: California Wind Capacity: Programs and Policies ................................................................................. 7-15

Figure 7 13: Illinois Wind Capacity: Programs and Policies ...................................................................................... 7-17

Figure 7 14: Massachusetts Wind Capacity: Programs and Policies ......................................................................... 7-18

Figure 7 15: Michigan Wind Capacity: Programs and Policies .................................................................................. 7-19

Figure 7 16: New Jersey Wind Capacity: Programs and Policies .............................................................................. 7-20

Figure 7 17: New York Wind Capacity: Programs and Policies ................................................................................. 7-21

Figure 7 18: Pennsylvania Wind Capacity: Programs and Policies ............................................................................ 7-22

Figure 7 19: Wisconsin Wind Capacity: Programs and Policies ................................................................................ 7-23

Figure 7 20: New Landfill and Dairy and Swine Digester Biogas Capacity by State (1999-2013, kW) ...................... 7-24

Figure 7 21: California Biogas Capacity: Programs and Policies ............................................................................... 7-25

Figure 7 22: Connecticut Biogas Capacity and Programs and Policies...................................................................... 7-27

Figure 7 23: Illinois Biogas Capacity: Programs and Policies .................................................................................... 7-28

Figure 7 24: Massachusetts Biogas Capacity: Programs and Policies ....................................................................... 7-29

Figure 7 25: Michigan Biogas Capacity: Programs and Policies ................................................................................ 7-30

Figure 7 27: New Jersey Biogas Capacity: Programs and Policies ............................................................................. 7-31

Figure 7 27: New York Biogas Capacity: Programs and Policies ............................................................................... 7-33

Figure 7 28: Pennsylvania Biogas Capacity: Programs and Policies .......................................................................... 7-34

Figure 7 29: Wisconsin Biogas Capacity: Programs and Policies .............................................................................. 7-35

Figure E 1: Food Store Segment Annual Average Weekday Hourly Electric End-Use Loads ...................................... E-9

Figure E 2: Health Segment Average Annual Weekday Hourly Natural Gas End Use Loads .................................... E-10

Figure E 3: Health Segment Average Annual Weekday Hourly Electric End Use Loads ............................................ E-10

Figure E 4: Technical Potential with and without Absorption Chilling Option ......................................................... E-11

Figure E 5: Technical Potential under Different Annual Operating Hour Constraints .............................................. E-13

Figure E 6: Comparisons of Market Segment Technical Potentials, ICF 2011 and Itron ........................................... E-14


LIST OF TABLES | vii

LIST OF TABLES

Table 2 1: 2014 New CHP Technical Potential (MW) by Segment and IOU ................................................................ 2-5

Table 2 2: Technical Potential Wind Capacity with GCF > 35% by County (MW) ....................................................... 2-7

Table 2 3: Landfill Gas Technical and Economic Potential by County ......................................................................... 2-9

Table 2 4: Dairy Biogas Economic and Technical Potential by County ....................................................................... 2-9

Table 2 5: Wastewater Treatment Plants Biogas Economic and Technical Potential by County ............................. 2-10

Table 2 6: AES Technical Potential Based on Maximum Power by Market Segment and IOU ................................. 2-12

Table 2 7: Economic Potential for AES by Market Segment and IOU by 2024 ......................................................... 2-12

Table 3 1: DER Projects and Capacity in California at 12/31/14 ................................................................................. 3-5

Table 4 1: Summary of Survey Groups ..................................................................................................................... 4-11

Table 4 2: Key Research Topics ................................................................................................................................. 4-14

Table 5 1: Summary of Barriers to SGIP Technology Market Adoption .................................................................... 5-22

Table 5 2: Realized or Perceived Drivers to Increased Growth in SGIP Technologies .............................................. 5-23

Table 5 3: Expected Outcomes by Timeframe .......................................................................................................... 5-24

Table 6 1: 2014 New CHP Technical Potential (MW) by Segment and IOU ................................................................ 6-3

Table 6 2: PG&E 2014 Technical Potential by Segment and System Size Category (MW).......................................... 6-8

Table 6 3: SCE 2014 Technical Potential MW by Segment and System Size Category (MW) ..................................... 6-8

Table 6 4: SDG&E 2014 Technical Potential MW by Segment and System Size Category (MW)................................ 6-9

Table 6 5: 2014 and Future CHP Technical Potentials by IOU (MW) .......................................................................... 6-9

Table 6 6: Wind Power Classes and Speeds .............................................................................................................. 6-19

Table 6 7: Area by Wind Class Speed (square miles): 50 Meter Height .................................................................... 6-19

Table 6 8: Potential Wind Capacity with GCF > 35% by County (MW) ..................................................................... 6-22

Table 6 9: Counts of Indicator Businesses for Distributed Wind Systems by Type by County ................................. 6-24

Table 6 10: Number of Potential Installations of 50 kW and 1.5 MW Wind Turbines.............................................. 6-25

Table 6 11: Technical Wind Potential by IOU: (50 Meters) ...................................................................................... 6-26

Table 6 12: Economic Potential for Distributed Wind Energy .................................................................................. 6-28

Table 6 13: Landfill Gas Technical and Economic Potential by County ..................................................................... 6-34

Table 6 14: Distribution of Nominal 500 kW IC Engines by IOU ............................................................................... 6-36

Table 6 15: Dairy Biogas Economic and Technical Potential by County ................................................................... 6-38

Table 6 16: Wastewater Treatment Plants Biogas Economic and Technical Potential by County ........................... 6-41

Table 6 17: Technical, Economic, and Market Biogas Generation Potential by IOU ................................................ 6-44

Table 6 18: AES Technical Potential, Based on Energy Capacity to Shave Top 4 Hours ........................................... 6-47

Table 6 19: Economic Potential for AES by Market Segment and IOU by 2024 ....................................................... 6-50

Table 6 20: Summary of DG and Energy Storage Potentials vs. Installed Capacities in CA ...................................... 6-52

Table 7 1: Count of Programs in DSIRE Data by State and Implementation Sector ................................................. 7-37

Table 7 2: Count of Primary Driver Programs by State and Technology .................................................................. 7-38

Table 7 3: Mapping of DSIRE Program Types to Different Aggregation Levels ......................................................... 7-39

Table 7 4: Parameter Estimates for Model of SGIP CHP Impacts ............................................................................. 7-43

Table 7 5: California CHP Capacity and Estimated Influence of SGIP ....................................................................... 7-44


LIST OF TABLES | viii

Table C 1: Listing of Programs or Policies Impacting DG and Storage in California ................................................... C-1

Table C 2: Summary of Primary Programs or Policies Impacting DG and Storage in California ................................ C-2

Table C 3: Policies Specifically Related to SGIP .......................................................................................................... C-9

Table D 1: Commercial and Industrial NAICS Codes and Associated SitePro Building Type ...................................... D-1

Table E-1: Market Segment Annual Growth Rates ..................................................................................................... E-2

Table E-2: Commercial and Industrial Census Data Fields .......................................................................................... E-2

Table E-3: Residential Energy Consumption Survey Data Fields................................................................................. E-3

Table E 4: Estimated Building Populations Market Segment, Climate Region, and IOU ............................................ E-3

Table E 5: CHP System Performance Assumptions ..................................................................................................... E-4

Table E 6: Engineering Performance Assumptions ..................................................................................................... E-6

Table E 7: Commercial and Industrial 2012 NAICS Codes and Associated SitePro Building Type ............................ E-16


GLOSSARY | ix

GLOSSARY

Term Definition

Global Terms

AWEA American Wind Energy Association

BOP Balance of plant

CAISO California independent System Operator

Cal EPA California Environmental Protection Agency

CARB California Air Resources Board

CCDC California Clean Energy DG Coalition

CEA Clean Energy Coalition

CEC California Energy Commission

CEJP Clean Energy Jobs Program

CESA California Energy Storage Alliance

CSE Center for Sustainable Energy

CPUC California Public Utilities Commission

DRP Distributed Resource Plans

HHV Higher Heating Value

IEPR Integrated Energy Policy Report

IOU Investor-owned utility

LHV Lower Heating Value

LMOP Landfill Methane Outreach Program

NAICS North American Industry Classification System

NEM Net energy metering

NREL National Renewable Energy Laboratory

NIMBY Not In My Backyard

PA Program Administrator

PG&E Pacific Gas & Electric

PY Program year

SCE Southern California Edison Company

SCG Southern California Gas Company

SDG&E San Diego Gas & Electric Company

SGIP Self-Generation Incentive Program

Technologies

AES Advanced energy storage

CHP Combined heat and power

DER Distributed energy resource

DG Distributed generation

FC Fuel cell

GT Gas turbine

IC engine Internal combustion engine


GLOSSARY | x

Term Definition

MT Microturbines

ORC Organic Rankine Cycle

PRT Pressure reduction turbine

PV Photovoltaic

WD Wind turbine

WWTP Wastewater Treatment Plant

Economics/Financing

EPBB expected performance based buydown

ITC Investment Tax Credit

LCOE levelized cost of energy

MACRS Modified Accelerated Cost-Recovery System

PBI performance based incentive

PCT participant cost test

PPA power purchase agreement

PTC Production tax credit

STRC societal total resource cost

TRC total resource cost

Emissions Related

GHG greenhouse gas

CO2eq CO2 equivalent

NOx Nitric oxide (NO) and nitrogen dioxide (NO2)

PM10 Particulate matter (PM) with diameter of 10 micrometers or less

SO2 Sulfur Dioxide

REC renewable energy credit

EXECUTIVE SUMMARY| 1-1

1 EXECUTIVE SUMMARY

1.1 Purpose of This Report

The Self Generation Incentive Program (SGIP) is a long running California incentive program supplying

support to distributed generation (DG) and energy storage technologies deployed on the customer side

of the meter. Assembly Bill 970 established the SGIP in 2001 to respond to California’s electricity crisis of

2000-2001, and to help encourage development of DG resources. Since its inception in 2001, the

program has been extended several times and has undergone a number of significant changes. Most

notably, changes have been made in the types of technologies and fuels eligible to receive incentives

under the program, and goals have been added targeting reductions in greenhouse gas (GHG) emissions.

In its September 2011 decision modifying the SGIP and implementing Senate Bill 412, the California

Public Utilities Commission (CPUC) established market transformation as one of the four goals of the

program.1 The primary purpose of this report is to identify the extent to which the SGIP is transforming

California’s DG and energy storage markets.

In addition, this study has seven research objectives:

» Characterize the market for SGIP technologies in California;

» Identify specific DG and energy storage market barriers and market strengths;

» Identify and describe SGIP and other DG and energy storage policy interventions;

» Assess the effects of the SGIP and other policy interventions in reducing identified market barriers and in supporting the transformation of DG and energy storage markets toward self-sufficiency;

» Establish and analyze indicators (metrics), that reflect the evolution of the distributed energy markets toward self-sufficiency;

» Assess sustainability of these markets in California in the absence of the SGIP; and,

» Identify and recommend changes to the SGIP program to support further development of these markets in California.

California’s DG and energy storage markets encompass a wide variety of market players and

stakeholders who have differing roles. We used a combination of in-depth interviews and online surveys

to obtain perspectives from past and current SGIP Program Administrators (PAs), CPUC program staff,

DG and energy storage technology manufacturers and project developers, customers who either did or

did not participate in the SGIP but may have installed DG or energy storage technologies on their

premises, public agencies and nonprofit organizations involved with DG or energy storage. In particular,

we wanted to obtain their perspectives on how the DG and energy storage markets function in

California, what have been and continue to be key barriers or opportunities, to what extent the SGIP has

influenced DG and energy storage market growth, and their thoughts on ways in which the SGIP can

further help support DG and energy storage growth.

1 In particular, SGIP is to assist in market transformation of distributed energy resource (DER) technologies. See CPUC

Decision D.11-09-015, September 8, 2011.



In assessing the status of California’s progress toward a transformed market, we also estimated

technical, economic, and market potentials for different SGIP technologies and compared them to 2014

levels of installed DG and energy storage technologies.

To help assess what influence the SGIP has on DG market growth, we compared growth in California’s

DG market to that of eight other states (Connecticut, Massachusetts, Michigan, New Jersey, New York,

Pennsylvania, and Wisconsin).2 We also conducted statistical analyses to see if we could quantify the

influence of the SGIP on DG market growth.

Based on the information learned from this work, we offer the following conclusions and

recommendations.

1.2 Conclusions

1. The SGIP was not created as a market transformation program and market transformation was not explicitly added as a goal to the SGIP until 2011. In addition, program theory and logic has never been developed for the SGIP. As a result, we developed a preliminary program theory and logic model focused on how the SGIP can play a role in transforming California’s DG and energy storage markets.

a. DG and energy storage market transformation can be defined as follows: “long-lasting sustainable changes in the DG/energy storage markets achieved by reducing barriers to the adoption of DG/energy storage technologies and effecting structural changes in the DG/energy storage markets, and behaviors of market actors such that the market can be sustained without publicly-funded intervention.”

b. The program theory and logic model includes short, medium and long term outcomes for the program and identifies SGIP related activities and outputs to help achieve DG and energy storage market transformation..

2. In spite of the lack of existing market transformation goals, the SGIP has had a significant impact on the growth of customer-sited DG resources in California and appears poised to have a significant impact on the growth of customer-sited energy storage.

a. Since the inception of the SGIP in 2001, the growth in customer-sited combined heat and power (CHP) has increased from 4 MW in 2001 to nearly 290 MW by the end of 2014. California’s contribution to customer-sited CHP is significantly greater than that found in other states with CHP programs. For example, New York has the second largest amount of installed CHP next to California, but that represents only 43% of the CHP capacity installed in California.

b. Historically, there has been limited growth in distributed wind in the SGIP; however, since 2010, there has been an increase in distributed wind capacity installed under the SGIP. In comparison to the eight other states examined, with approximately 25 MW of distributed

2 Our initial intent was also to compare growth of energy storage in California to growth of energy storage in other states.

However, as energy storage is a nascent technology, there was insufficient information available on installed energy storage capacities in other states. As a result, we focused only on the DG market for this statistical comparison.



wind, California ranks second only to Massachusetts in the amount of distributed wind generation capacity installed since 1999.

c. Based on EPA data, California ranks first for installation of distributed biogas generation between 1999 and 2014. While it cannot be shown statistically that the SGIP by itself led to growth of biogas in California, the SGIP certainly helped contribute to that growth. Growth in onsite biogas generating capacity in California increased from less than 100 kW in 2002 to more than 32 MW by 2014.

d. Growth in advanced energy storage (AES) has been rapid under the SGIP. Since 2010, over 10 MW of customer-sited energy storage has been installed under the SGIP and there are over 220 AES projects currently in the SGIP queue to receive incentives.

3. There is substantial opportunity to grow customer-sited DG and energy storage in California.

a. The economic potential for customer-sited CHP in California is 15.5 GW. The highest opportunities are associated with food manufacturing/processing operations, restaurants, food stores, the health and hospital market segments, and large office buildings. Prime movers that appear to provide the most cost effective approach for CHP include internal combustion (IC) engines and gas turbines.

b. We estimate distributed wind energy economic potential in California at nearly 9.8 GW. This economic potential assumes use of larger wind turbines, rated in the 1-3 MW size range but located next to customer sites to help meet onsite electrical demand. Nearly three fourths of the economic potential is located in the SCE service territory, commensurate with the large amount of area in SCE with higher class wind speeds.

c. There is approximately 665 MW of small scale biogas economic potential in California. Small scale landfill gas generation represents about 400 MW or over 60% of the state’s biogas economic potential, with the remainder associated with wastewater treatment digester and onsite dairy digester potential.

d. While advanced energy storage has enormous technical potential, especially among the residential sector, we estimate the economic potential prior to 2024 at approximately 5.3 GW. At 2.8 GW, SCE has the largest economic potential while PG&E’s economic potential is estimated only slightly lower at 1.9 GW. The largest economic potential is in the large office sector.

4. Statistical analyses to compare the effect of SGIP on DG growth relative to other states showed that the SGIP has had an impact on market growth in CHP but was inconclusive with respect to distributed wind and biogas generation growth. In addition, due to a lack of consistent data on energy storage installations between the different states, we were unable to analyze the impacts of the SGIP on advanced energy storage.

a. Modeling shows that SGIP led to a statistically significant increase in the installed capacity of CHP in California from 2002 to 2007. The statistical model found that local financing programs also had a positive impact on installed CHP capacity. In contrast, net energy metering (NEM) policies were found to result in a decrease in CHP. We theorize that implementation of California’s NEM policy, which has not included CHP, has steered investors toward those resources which do qualify for NEM. For example, implementation of



NEM could lead to more investment in photovoltaics (PV) and a reduction in investment in CHP due to the NEM improving the relative cost effectiveness of PV.

b. Growth in distributed wind and biogas generation in California appears to track SGIP funding. However, the statistical modeling was not able to produce stable estimates of the impact of SGIP on wind or biogas capacity beyond the effects of other programs and policies.

5. Interviews with Program Administrators within the SGIP, policy makers and those responsible for similar programs outside of California uncovered several important observations:

a. Market transformation goals are critical to ensuring the program is headed in the right direction, ensuring measureable progress and setting a clear end point.

b. Addressing unmet market and customer needs are critical in obtaining the widespread market adoption needed to transform the DG and energy storage markets. Customer use cases help identify customer energy usage and needs by classes of customers. In selecting how DG and energy storage technologies are provided through the program, it is important to match technologies to customer use cases.

c. Market growth of DG and energy storage will occur more readily when the technologies achieve customer needs, utility needs and technology provider needs. DG and energy storage technologies have the potential to help utilities provide safe, reliable and cost effective energy to their customers. Under the SGIP, the utilities are tasked with managing the program, rather than helping develop DG and energy storage technologies in a partnership arrangement with technology providers. This partnership can result in economic benefits to the customer, the utility, and the technology provider and help accelerate market adoption of DG and energy storage technologies.

d. Program Administrators should play a significant role in the strategic planning of how to implement market transformation of DG and energy storage technologies. Because of their market connection to utility customers and understanding of utility operations, the PAs bring a unique perspective on the role of DG and energy storage in a transformed market.

e. Technology reliability is important in how prospective program applicants view the market. In the Northeast, the PAs used a “prescriptive” catalog approach that standardized configurations, costs and installation procedures to be used by project developers when installing CHP systems. This standardized approach reduced risk, helped streamline the application and implementation processes, and helped reduce costs. At the same time, this approach may limit a program’s ability to support emerging technologies.

f. Successful market transformation involves more than addressing economic barriers. Market barriers must be taken into account and alignment of state policies to promote DG and energy storage is very important. As one respondent pointed out, the correct alignment of policies provides investors with faith in the market and is ultimately what moves market transformation forward.



1.3 Recommendations

This report includes a first attempt at developing an SGIP program theory and logic model that targets

transformation of California’s DG and energy storage markets. Based on collected survey information,

we identified barriers as well as opportunities to expand California’s DG and energy storage markets.

However, there has been little discussion among the SGIP PAs, policy makers, and stakeholders as to

what would constitute transformed DG and energy storage markets in California. Transforming

California’s DG and energy storage markets will require substantive discussion of the role of the SGIP

versus other market interventions and the role of the utilities in pursuing this market transformation.

Specific recommendations in this area include the following:

1. The collected survey information in this study along with the preliminary program theory and logic model could be used as a starting point to form the basis of future quantitative goals for the program such as technology cost reductions, penetration levels, or levelized costs of electricity.

2. As some PAs have pointed out, the SGIP as configured may not adequately provide for a partnership between utilities and project developers in addressing both customer and utility needs. However, such a partnership could significantly help make forward progress in transforming California’s DG and energy storage markets. Moreover, the successful transformation of California’s DG and energy storage markets requires a uniform front of policies that meet the needs of the industry and the utilities. Identifying commonalities between customer, project developer and utility needs, and then developing uniform policies to help achieve these needs should be a key objective of a plan for transforming California’s DG and energy storage markets.

3. California PAs have noted that SGIP could be improved by better matching of SGIP objectives to customer use cases. One example is matching of SGIP technology selection and incentives to utility customer energy, financial, and environmental needs. Matching program objectives to customer needs should be another key component of the SGIP moving forward.

4. Based on the vision of what constitutes a transformed DG and energy storage market in California, the California investor-owned utilities (IOU)s, PAs, the CPUC, and other appropriate state agencies should work together to develop the goals of the transformed market and an action plan outlining the strategies, objectives, and tactics that will be employed to achieve these goals.

5. The recently completed SGIPce Cost Effectiveness Model should be used by the SGIP Working Group to develop strategies for combining DG and energy storage technologies that meet customer needs, utility needs, and overall societal needs. Use of the SGIPce cost-effectiveness model will also provide for a better understanding of how incentives can be used as a tactic to achieve the desired goals and objectives.

6. Program outreach is important in helping move toward a transformed market. After market transformation goals are defined, the SGIP PAs should perform targeted outreach to likely adopters of DG and energy storage technologies. For CHP, gas bill data can be leveraged to identify customers with high heating loads. Similarly, wind resource data can be utilized to identify areas on the urban-rural boundary where behind-the-meter wind turbines can be installed. Finally, biogas producers should be identified, especially those facilities that would



otherwise vent biogas to the atmosphere as they provide the largest potential for greenhouse gas reductions.

7. The ability for DG and energy storage technologies to “take off” requires buy-in from the investor community. In order to create investor confidence, potentially conflicting policies (such as DG and energy storage incentives and standby charges) should be reviewed to ensure alignment toward a strategic goal.

SUMMARY OF RESULTS | 2-1

2 SUMMARY OF RESULTS

The primary goal of this study is to identify the extent to which the SGIP is transforming California’s

distributed generation and energy storage markets. Based on the findings, this study is also to provide

recommendations on ways that the SGIP can help further support DG and energy storage market

transformation in California.

In addition to the overall goal, this study has seven research objectives:

» Characterize the market for SGIP technologies in California;

» Identify specific DG/energy storage market barriers and market strengths;

» Identify and describe SGIP and other DG/energy storage policy interventions;

» Assess the effects of the SGIP and other policy interventions in reducing identified markets barriers and in supporting the transformation of the SGIP—eligible technology markets toward self-sufficiency;

» Establish and analyze indicators, or metrics, that reflect the evolution of the distributed energy markets toward self-sufficiency;

» Assess sustainability of these markets in California in the absence of the SGIP; and,

» Identify and recommend changes to the SGIP program to support further development of these markets in California.

This section provides a summary of the results with respect to the overall goal and research objectives.

Extent to which SGIP is Transforming California’s DG and Energy

Storage Markets

In order to assess the extent to which the SGIP has helped transform California’s DG and energy storage

markets, we need a definition of what constitutes a transformed DG and energy storage market.

We found no existing definition or characterization of transformed DG and energy storage markets. This

is not surprising because the SGIP was not created as a market transformation program. Established in

2001 in response to California’s electricity crises of 2000, the SGIP was primarily focused on developing

distributed generation projects as a means to help offset peak demand at utility customer sites. Market

transformation first showed up as one of four goals of the SGIP in a 2011 decision by the CPUC.1

In addition, a program theory and logic model had not been developed for the SGIP. Program theory and

logic establishes clear expectations about what constitutes the transformed markets, what barriers are

preventing transformation of the market and the interventions needed to overcome the barriers. The

program logic also can identify desired outcomes associated with transforming the market and activities

and outputs that can help market transformation move forward.

1 In decision D.11-09-015 (“Decision Modifying the Self-Generation Incentive Program and Implementing Senate Bill 412,”

September 8, 2011), the CPUC established these goals as the “Statement of Purpose” for the SGIP.



The CPUC has provided guidance on defining transformed energy efficiency markets. We adapted that

definition to develop the following definition of transformed DG and energy storage markets:

…long-lasting sustainable changes in the DG/energy storage market achieved by

reducing barriers to the adoption of DG/energy storage technologies and effecting

structural changes in the DG/energy storage market and in the behaviors of market

actors such that the market can be sustained without publicly-funded intervention.

Establishing SGIP Program Theory and Logic

In order to continue forward progress of the SGIP in transforming California’s DG and energy storage

markets, we developed a preliminary program theory and logic model for the program. Figure 2-1 shows

the preliminary program theory and logic diagram developed for the SGIP.

Figure 2-1: Preliminary SGIP Program Theory and Logic Diagram



Based on our adopted definition of transformed DG and energy storage markets, we propose that

transformed DG and energy storage markets will have the following key elements:

» There will be no significant barriers preventing utility customers and utilities from routinely using DG and energy storage technologies as part of their energy solutions;

» Changes in market operation along with performance and cost improvements will allow DG and energy storage to be adopted without incentives;

» The market will encourage development and adoption of even more efficient DG or energy storage technologies, services and solutions into the market;

» The DG and energy storage markets will continue to operate and grow even if public interventions are modified, refocused or reduced;2 and,

» The DG and energy storage markets will develop in ways to increase environmental benefits, sustain employment and lead to increased job creation.

Based on interviews with past and current SGIP Program Administrators, host customers, key market

players, and stakeholders, we propose the following long term outcomes for the SGIP to help transform

California’s DG and energy storage markets:

» DG and energy storage markets grow without incentives while policies affecting the markets are aligned to help support continued growth;

» DG and energy storage make up a significant percentage (e.g.,15%) of California’s electricity mix3;

» DG and energy storage help provide valuable environmental benefits that include reductions in criteria air pollutants, improved water quality and land use and reductions in GHG emissions; and,

» DG and energy storage markets contribute to sustained employment and new job creation.

The complete set of analyses on program theory and logic is contained in Section 5 of the report.

Assessing SGIP’s Influence

In spite of the lack of existing market transformation goals, we found the SGIP has had a significant

impact on the growth of customer-sited DG resources in California and appears poised to have a

significant impact on the growth of customer-sited energy storage. Among the impacts are the

following:

1. Since the inception of the SGIP in 2001, the growth in customer-sited combined heat and power (CHP) has increased from 4 MW in 2001 to nearly 290 MW by the end of 2014. California’s contribution to customer-sited CHP is significantly greater than that found in other states with CHP programs. For example, New York has the second largest amount of CHP installed next to

2 This characterization comes from Ken Keating’s guidance to the CPUC on transforming the energy efficiency market. See

Keating, Ken, “Guidance on Designing and Implementing Energy Efficiency Market Transformation Initiatives,” October 13, 2014, page 2

3 Note that if DG makes up all of the 12,000 MW by 2020 goal of Governor’s Brown Clean Energy Jobs Plan, the 6500 MW goal by 2032 for CHP reaches 3000 MW by 2020, and 200 MW of customer-sited storage is achieved by 2024, this would be represent over 30% of California’s peak demand in 2014 of 45,089 MW. It would also represent approximately 19% of the 78,865 MW of California’s in-state electricity generation capacity installed as of the end of 2014 (see http://energyalmanac.ca.gov/electricity/electric_generation_capacity.html).



California, but New York’s CHP capacity represents only 43% of the CHP capacity installed in California.

2. Historically, there has been limited growth in distributed wind in the SGIP. However, since 2010, there has been an increase in distributed wind capacity installed under the SGIP. In comparison to eight other states examined, California (with approximately 25 MW of distributed wind) ranks second only to Massachusetts for the amount of distributed wind generation capacity installed since 1999.

3. Based on EPA biogas capacity data, California ranks first for installation of distributed biogas generation between 1999 and 2014. Helped by incentives provided by the SGIP, growth in onsite biogas generating capacity in California increased from less than 100 kW in 2002 to more than 32 MW by 2014.

4. Growth in advanced energy storage (AES) has been rapid under the SGIP. Since 2010, over 10 MW of customer-sited energy storage has been installed under the SGIP, and there are over 220 AES projects currently in the SGIP queue.

We also sought to determine the extent of SGIP’s influence on DG market growth by comparing it to

similar DG support programs operated elsewhere in the country. We statistically compared growth of

CHP, wind and biogas capacity in California against growth of those same technologies in eight other

states (New York, Massachusetts, Illinois, Wisconsin, Michigan, New Jersey, Pennsylvania and Illinois).

Due to the lack of consistent energy storage data among the different states, we were not able to

investigate the impact of the SGIP the growth of energy storage. However, with respect to CHP, wind

and biogas, we found the following:

1. Modeling shows that SGIP led to a statistically significant increase in the installed capacity of CHP in California from 2002 to 2007. The statistical model found that local financing programs also had a positive impact on installed CHP capacity. In contrast, net energy metering (NEM) policies were found to result in a decrease in CHP. We theorize that implementation of a NEM policy that does not include CHP may lead to preferential investment in NEM resources. This preferential investment occurs because of the opportunity to recognize financial benefits resulting by netting excess generation. For example, implementation of NEM could lead to more investment in photovoltaics (PV) and a reduction in investment in CHP due to NEM improving the relative cost effectiveness of PV.

2. Growth in distributed wind and biogas generation in California appears to track SGIP funding. However, statistical modeling was not able to produce stable estimates of the impact of SGIP on wind or biogas capacity beyond the effects of other programs and policies.

The full set of analyses on assessing SGIP’s influence on DG market transformation is contained in

Section 7 of the report.

Characterizing California’s DG and Energy Storage Markets

In characterizing California’s DG and energy storage markets, we provide estimates of the amount of DG

or energy storage capacity that could be installed in different market segments to meet the energy

needs of that market segment. We estimate technical, economic and market potentials. The full set of

results on market characterization are presented in Section 6.



California’s Combined Heat and Power (CHP) Market

California has approximately 24 GW of technical potential associated with new CHP smaller than 5 MW

in capacity. Table 2-1 summarizes the CHP technical potential by market segment and investor owned

utility (IOU) service territory.

Table 2-1: 2014 New CHP Technical Potential (MW) by Segment and IOU

Segment PG&E SCE SDG&E TOTAL Percent

College 54.7 106 12.79 174 0.72%

Food Manufacturing 4,321 3,278 280 7,879 32.51%

Food Store 988 1,489 294 2,771 11.43%

Health, Hospital 1,120 1,237 222 2,579 10.64%

Large Multifamily 360 595 118 1,073 4.43%

Lodging, Hotel 644 634 204 1,482 6.11%

Office, Large 913 1,169 195 2,277 9.40%

Office, Small 52.2 62.38 18.38 133 0.55%

Restaurant, Sit-Down 829 1,737 390 2,955 12.19%

Retail, Large 539 833 179 1,551 6.40%

Small Multifamily 0.00 0.00 0.00 0.00 0.00%

School 5.22 5.58 1.49 12.3 0.05%

Warehouse 702 570 79.0 1,351 5.57%

TOTAL 10,527 11,717 1,992 24,236 100%

Percent 43% 48% 8% 100%

Based on a complementary study conducted on SGIP technology cost effectiveness,4 we estimate the

economic potential for CHP at 15.5 GW, or 64% of the 24.2 GW of 2014 new technical potential.

We use a combination of historical California CHP installation capacity, logistic modeling and a starting

assumption of a future market potential in estimating CHP market growth.5 The resulting 2024 market

potential is 4.5 GW. Figure 2-2 shows CHP market growth out through 2070 needed to achieve the 4.5

GW target. However, along the way CHP growth hits two important policy targets: the AB32 target of

1,844 MW by 2020 and the Clean Energy Jobs Plan (CEJP) target of 2,594 MW by 2030.6

4 Itron on behalf of PG&E and the SGIP Working Group, “2015 Self-Generation Incentive Program Cost Effectiveness Study,”

October 2015; final report pending release

5 Our method is a hybrid Delphi approach wherein we start with target market potentials based on previous potential studies,

develop market growth curves using the historical CHP capacities and calculate annual CHP growth rates. We then assess the resulting average annual growth rate against that of other technologies to determine if the growth rate appears reasonable. If not we adjust the future market potential and recalculate the growth rates.

6 The AB 32 CHP target for CHP is 4,000 MW by 2030 while Governor Brown’s Clean Energy Jobs Plan targets 6,500 MW of

CHP by 2030. New CHP additions in California under both AB 32 and the CEJP will include capacities greater than the 5 MW upper limit examined in this study. To estimate the portion of the 4,000 MW and 6,500 MW of new additions from AB 32 and the CEJP that will be in the 30-5000 kW capacity range, we consider proportions among new capacity in CA since 1999. About 30% of this new capacity since 1999 has been in the 30-5000 kW range. Consequently, for the AB 32 portion, we estimate that 30% or 1,200 MW of the 4,000 MW target from AB 32 and 30% or 1,950 of the CEJP 6,500 MW target will be in the 30-5000 kW range. When added to the 2014 existing 644 MW of capacity the target is 1,844 for the AB 32 target and 2,594 MW for the CEJP target in the 30-5000 kW range.



Figure 2-2: Growth in Cumulative CHP Capacity toward 2024 Market Potential



California’s Wind Market

We estimate California’s wind technical potential for wind turbines of less than 5 MW in capacity at

approximately 16 GW as shown in Table 2-2 below.

Table 2-2: Technical Potential Wind Capacity with GCF > 35% by County (MW)

County

Potential Capacity

County

Potential Capacity

(MW) (MW)

Alameda 33 Orange 68

Alpine 142 Placer 36

Amador 4 Plumas 105

Butte 22 Riverside 1,788

Calaveras 3 Sacramento 0

Colusa 10 San Benito 7

Contra Costa 24 San Bernardino 4,451

Del Norte 107 San Diego 786

El Dorado 58 San Francisco 1

Fresno 103 San Joaquin 4

Glenn 4 San Luis Obispo 56

Humboldt 268 San Mateo 27

Imperial 374 Santa Barbara 596

Inyo 1,698 Santa Clara 1

Kern 1,378 Santa Cruz 1

Kings 1 Shasta 246

Lake 24 Sierra 77

Lassen 192 Siskiyou 448

Los Angeles 1,127 Solano 139

Madera 33 Sonoma 27

Marin 43 Stanislaus 5

Mariposa 4 Sutter 6

Mendocino 73 Tehama 64

Merced 62 Trinity 83

Modoc 177 Tulare 125

Mono 498 Tuolumne 76

Monterey 65 Ventura 375

Napa 6 Yolo 16

Nevada 19 Grand Total 16,166

However, based on cost effectiveness results and assuming that distributed wind generation is located

on the customer side of the meter, we estimate California’s distributed wind economic potential at

approximately 9.8 GW.

In assessing California’s wind market potential, we investigated different average annual growth rates.

Figure 2-3 shows cumulative California wind capacity assuming an average annual growth rate of 20%.

Under that scenario, cumulative wind capacity reaches approximately 1.2 GW by 2038. This represents



over 12% of the statewide economic potential estimated earlier assuming installation of 1.5 MW wind

turbines at representative businesses.

Figure 2-3: Wind Market Potential at 20% Average Annual Growth Rate

California’s Biogas Market

California’s biogas market is derived primarily from biogas generated at landfills, dairies and wastewater

treatment facilities. Overall, we estimate the technical potential of California’s biogas market to be over

720 MW and the economic potential to be over 660 MW.

We found that California has over 450 MW of untapped landfill gas technical potential and a little over

400 MW of that capacity is a cost effective resource. Table 2-3 is a summary of the technical and

economic potential for landfill gas in California.



Table 2-3: Landfill Gas Technical and Economic Potential by County

County

Total Economic Potential

Capacity (MW)

Total Technical Potential

Capacity (MW)

Los Angeles 185 190

Orange 32 32

San Diego 26 28

Alameda 21 27

Sacramento 18 18

Ventura 18 18

Santa Clara 16 23

San Mateo 13 13

Contra Costa 11 11

Sonoma 10 10

Others 53 88

Grand Total 404 457

California has over 1.8 million dairy cows, making it one of the largest dairy producing states in the

country. We found that the statewide technical and economic potential of dairy biogas is 137 MW and

136 MW respectively. Table 2-4 is a summary of California’s dairy biogas technical and economic

potential by county.

Table 2-4: Dairy Biogas Economic and Technical Potential by County

County

Total Number of

Dairies


Capacity (kW)


Capacity (kW)

Tulare 281 37,251 37,251

Merced 228 21,258 21,258

Kings 119 14,137 14,137

Stanislaus 207 13,837 13,837

Kern 51 12,873 12,873

Fresno 79 8,995 8,995

San Joaquin 113 7,918 7,918

Madera 43 6,033 6,033

Imperial 62 3,960 3,960

Riverside 28 3,042 3,042

Sonoma 65 2,148 2,148

Glenn 31 1,287 1,287

Sacramento 30 1,084 1,084

Humboldt 60 1,037

Marin 25 585 585

San Bernardino 3 398 398

Tehama 11 269 269

Del Norte 7 217 217

Yuba 4 215 215

San Diego 3 148 148

Siskiyou 3 51

Grand Total 1,453 135,654 136,742



California has over 240 wastewater treatment plants that handle wastewater from communities across

the state. We estimate the statewide technical and economic potential for biogas from wastewater

treatment plants at 130 MW and 125 MW respectively. Table 2-5 is a summary of the technical and

economic potential for biogas from California’s wastewater treatment plants.

Table 2-5: Wastewater Treatment Plants Biogas Economic and Technical Potential by County

County Number of Sites

Total Economic

Potential (kW)

Total Technical

Potential (kW)

Los Angeles 8 33,258 33,305

Orange 6 11,420 11,510

San Diego 6 9,895 10,173

Santa Clara 4 9,533 9,533


Alameda 7 6,732 6,732

Riverside 13 6,432 6,705

San Bernardino 10 4,354 4,476

Fresno 4 3,252 3,341

San Francisco 2 2,927 3,003

Contra Costa 4 2,704 2,965

Kern 7 2,263 2,552

Ventura 8 2,459 2,459

San Joaquin 4 2,352 2,352

Stanislaus 2 2,057 2,235

San Mateo 7 2,107 2,186

Solano 4 1,714 1,786

Santa Cruz 2 1,328 1,328

Others 45 12,196 15,194

Grand Total 146 125,264 130,127

We establish a biogas market potential based on a logistic model using historical biogas installation

capacities and a starting assumption that landfill and wastewater biogas growth achieves 40% of their

economic potential and diary biogas growth achieves 20% of its economic potential. This corresponds to

an overall market potential of 268 MW and is geared toward achieving the 250 MW target set out by SG

1122. Figure 2-4 shows the corresponding biogas market growth rate. The peak annual growth rate is 22

MW in 2019. The average annual growth rate in capacity is 17% whereas the cumulative generation

growth rate is about 34%. Note that the SB 1122 target of 200 MW is hit by 2030.7

7 SB 1122 has an overall target of 250 MW. However, 50 MW of the target is associated with biomass residues from forestry

residues. Because we are only considering biogas from landfills, wastewater treatment facilities and dairies, we reduced the target accordingly to 200 MW. Note that SB 1122 discusses biogas from “municipal organic waste diversion.” Due to the manner in which landfill gas to energy projects are designed and operated, we believe this refers to landfill biogas potential that has not yet been recovered.



Figure 2-4: California’s Biogas Market Growth Forecast

California’s Advanced Energy Storage Market

Technical potential for customer-sited AES exists primarily for customer energy management, either

through reduced peak demand charges or energy arbitrage (shifting energy consumption to lower bill

periods). Using a bottom up analytic approach similar to that employed for CHP, we estimate technical

potential for AES in California at close to 19 GW. Table 2-6 in California breaks out the potential by IOU

and market segment.



Table 2-6: AES Technical Potential Based on Maximum Power by Market Segment and IOU

Segment PG&E (MW) SCE (MW) SDG&E (MW) Total (MW) Percent

College 52 92 2 146 0.78%

Food Manufacturing 17 15 1 32 0.17%

Food Store 271 330 16 618 3.28%

Health, Hospital 193 214 8 415 2.20%

Large Multifamily 209 304 12 525 2.78%

Lodging, Hotel 123 120 5 249 1.32%

Office, Large 698 1,007 32 1,737 9.21%

Office, Small 208 252 13 473 2.51%

Restaurant, Sit-Down 368 473 16 857 4.55%

Retail, Large 655 935 49 1,639 8.70%

School 70 102 4 176 0.94%

Single Family 4,845 4,356 273 9,474 50.25%

Small Multifamily 879 895 39 1,813 9.62%

Warehouse 284 404 10 698 3.70%

Total 8,873 9,500 481 18,854 100%

Percent 47.06% 50.39% 2.55% 100%

Based on cost effectiveness results from a complementary study on SGIP technologies, we estimate the

economic potential of AES at approximately 5.3 GW. Table 2-7 provides estimates of California’s AES

economic potential broken down by IOU and market segment.

Table 2-7: Economic Potential for AES by Market Segment and IOU by 2024

Sector PG&E (MW) SCE (MW) SDG&E (MW) Total (MW) Percent

College 23 27 6 55 1.04%


Food Store 125 144 27 296 5.59%

Health, Hospital 124 134 27 285 5.38%

Lodging, Hotel 84 84 31 198 3.74%

Office, Large 805 1,263 254 2,322 43.79%

Office, Small 213 269 55 538 10.14%


Retail, Large 219 426 66 711 13.41%

School 29 38 7 73 1.38%

Warehouse 135 197 24 357 6.73%

Grand Total 1,942 2,814 548 5,303

Percent 36.61% 53.06% 10.33%



The market potential for AES is a fraction of economic potential. An EPRI energy storage potential study8

stated that “Although it is difficult to predict how consumers will respond to the relatively new energy

storage technologies, the analysis assumes that the mid-range of the energy efficient product adoption

rate (35%) is a reasonable proxy for direct customer adoption of energy storage systems.” We therefore

assumed market potential to be 35% of economic potential or 1,856 MW as our starting assumption in

estimating future market potential of AES.

Using the 1,856 MW as our starting estimated market potential, we used logistic modeling and historical

energy storage installations to calculate annual AES growth rates in the future. Figure 2-5 depicts the

market growth forecast for AES associated with meeting a market potential goal of nearly 1.9 GW. This

market growth shows AES meeting the 2024 AB 2514 goal for behind the meter energy storage 4 years

early—in early 2020.

Figure 2-5: AES Market Growth Forecast

8 Rastler, D, Electricity Energy Storage Technology Options. A White Paper Primer on Applications, Costs, and Benefits, EPRI,

1020676, December 2010; This study based market potential on technical not economic potential. However, the May 12, 2008 California Energy Efficiency Potential Study found market sizes of 27 to 41% of economic potential; in the same range as the 35% used here.



Overcoming Market Barriers

California has significant untapped economic and market potential for DG and energy storage

technologies. In order to find out barriers to achieving these potentials, we interviewed past and current

SGIP Program Administrators, CPUC staff involved with the program, companies that manufacture and

develop DG and energy storage projects, customers who have either participated in the SGIP or those

who did not participate but may have installed DG or energy storage systems in their facilities or homes,

and other stakeholders such as public agencies or non-profit organizations who are involved with

implementation or regulation of DG and energy storage technologies.

Based on those interviews, we developed key “takeaways” about market barriers and opportunities

from the different groups.

Key Takeaways from Host Customer Surveys

Within the host customer survey, we asked customers for open-ended responses with respect to their

perceptions about market barriers to achieving widespread adoption of SGIP technologies as well as

recommended changes in the SGIP to help effect increased market transformation of SGIP technologies.

Among the more significant responses were the following:

» Most customers felt that high upfront costs represent a barrier to widespread adoption of DG and energy storage technologies. They recognized that SGIP incentives helped to lower upfront costs for them but felt it was important for the SGIP to help find ways to help reduce the cost of these technologies in order to achieve widespread market adoption.

» Customers noted that most people are not familiar with SGIP technologies, their costs, their performance, or their benefits. Consequently, in order to help grow the DG and energy storage markets, they recommended that SGIP increase program outreach both with respect to SGIP technologies as well as various aspects of the SGIP itself to help educate consumers.

» Customers also noted that there can be a great amount of discrepancy in sizing, costing and installing of SGIP technologies. Customers who noted this recommended that the SGIP help develop standardization in configurations and installations of SGIP technologies. They believe this standardization would help reduce costs and improve customer comfort level in adopting the technologies. There were also recommendations that the SGIP help screen or pre-qualify contractors who install technologies under the program to help ensure high quality services. The expectation from host customers is that contractors associated with installing SGIP technologies provide them with independent and honest assessments.

» A number of customers also stressed that streamlining the incentive and utility processes are important in enabling higher adoption of SGIP technologies.

Key Takeaways from Manufacturer and Project Developer Interviews

Manufacturers and project developers are usually responsible for the design of the SGIP technologies

employed at host sites. They are also typically responsible for interacting with the utility on obtaining

the utility interconnection, working with the PAs on SGIP applications and acquiring any needed permits

from regulatory agencies. As such, they interact with a number of stakeholders including the customers



and have developed perceptions about the SGIP market in California, its barriers and opportunities for

market growth. Among the key takeaways from manufacturers and project developers are the

following:

» Delays and additional costs associated with utility interconnection is universally cited as a primary barrier to expanded adoption of SGIP technologies.

» The prospect of helping customers in avoiding demand charges is viewed by the AES industry as one of the biggest value proposition for AES. However, the lack of clarity in NEM policy is stranding the deployment.

» Departing load charges, standby charges and non-by-passable charges are the biggest barriers to CHP adoption as they significantly impact project economics and appear to the CHP industry to be an unjustified cost.

» For fuel cell manufacturers and project developers, the “zero” criteria pollutant emissions aspects of fuel cells, along with favorable footprint/power density provides them with a significant market opportunity, but requires additional education of customers. In addition, they believe these characteristics coupled with their ability to provide grid support applications including reactive power should make them attractive to utilities. However, high upfront costs remain a barrier.

» For wind manufacturers and project developers, the biggest barriers to increased wind growth in California are the difficulties associated with matching customer load with the availability of adequate wind resources, NIMBY attitudes, and restrictions on wind energy within existing NEM policies.

Overall PA/CPUC Perspective and Key Takeaways

The PAs are responsible for implementing the SGIP and the CPUC is involved in establishing policies on

the SGIP. Both the PAs and CPUC have developed perceptions regarding how the SGIP relates to the DG

and energy storage markets in California, what barriers need to be overcome to achieve more

widespread adoption of the technologies, and opportunities that exist for achieving that more

widespread adoption.

Nearly all of the PAs felt that there was a large untapped potential for SGIP to help grow the market for

SGIP technologies. Similarly, all expressed that there were a number of challenges involved in making

progress towards increased market growth but had different opinions about the types of challenges and

ways they could be addressed. For example, at least two of the PAs felt that utility tariffs could impede

growth of SGIP technologies by under-valuing the benefits provided by SGIP technologies. The PAs also

noted that customer perception that SGIP technologies are expensive or “risky” present significant

barriers to be overcome to achieve market growth. Some of the PAs also felt that SGIP funding

allocation to technologies was not equitable and may have unfairly advantaged some technologies;

thereby impeding growth of those technologies.

Several of the PAs felt they were taking a more proactive approach in conducting outreach or marketing

technology offerings customized to create mutual benefit for both customers and the utility. However,

they noted that more could be done to educate customers as well as policy makers as to the benefits of

SGIP technologies.



Key takeaways from the interviews with the PAs and CPUC staff include the following:

» While market transformation was not a goal at the start of the SGIP, market development of SGIP technologies has been occurring under the SGIP;

» There is a significant untapped potential for SGIP to help grow the market for SGIP technologies;

» The lack of customer familiarity with SGIP technology costs and performance has created the perception among customers that SGIP technologies are costly and risky. This perception poses a significant barrier to additional SGIP technology growth;

» It would be beneficial to educate customers and policy makers as to the costs, performance and benefits of SGIP technologies; and,

» There is a need for alignment of critical state policies on DG and energy storage in order to make forward progress on market transformation of these technologies. To be successful, the overarching policy goal of a transformed DG/energy storage market needs to be presented clearly and with a unified front. This unified front provides clean tech capital investors with faith that policies and regulations will not present uncertainty or barriers to market growth and will enable them to invest in innovative clean technology projects. This consistent alignment of clean tech policies with the subsequent capital investment from the tech capital investment community will ultimately be a key force in transforming the DG and energy storage markets.

Results of the various interviews are contained in Section 5 of the report. Survey forms used in the

study are contained in Appendix A while host customer survey results are contained in Appendix B.

Lessons Learned

In addition to interviewing SGIP PAs and program staff, we also interviewed Program Administrators

responsible for similar programs outside of California. Among the PAs interviewed are those responsible

for implementing significant DG and CHP programs in the eastern United States (e.g., New York,

Massachusetts, New Jersey, etc.). We wanted to find out their perspectives on DG barriers and what

lessons they learned in implementing programs and how that may compare to California. Based on the

combined interviews conducted earlier and with our interviews with PAs outside of California, we

developed the following list of learned lessons:

1. In setting out to transform a market, it is important to have defined the characteristics of the transformed market, and to have quantitative goals. The quantitative goals help define successful transformation of the market and allow measurement of progress towards the goals.

2. Addressing unmet market and customer needs are critical in obtaining the widespread market adoption needed to transform the DG and energy storage market. Customer use cases help identify customer energy usage and needs by classes of customers. In selecting how DG and energy storage technologies are provided through the program, it is important to match technologies to customer use cases.

3. Market growth of DG and energy storage will occur more readily when the technologies achieve customer needs, utility needs and technology provider needs. DG and energy storage technologies have the potential to help utilities provide safe, reliable and cost effective energy to their customers. Under the SGIP, the utilities are tasked with managing the program, rather than helping develop DG and energy storage technologies in a partnership arrangement with



technology providers. This partnership can result in economic benefits to the customer, the utility and the technology provider and help accelerate market adoption of DG and energy storage technologies.

4. Program Administrators should play a significant role in the strategic planning of how to implement market transformation of DG and energy storage technologies. Because of their market connection to utility customers and understanding of utility operations, the PAs bring a unique perspective on the role of DG and energy storage in a transformed market.

5. Technology reliability is important in how prospective program applicants view installing a technology and so affect how technologies are adopted in the market. In the Northeast, the PAs used a prescriptive catalog approach for CHP technologies that reduced risk, helped streamline the application and implementation processes and helped reduce costs. At the same time, this approach needs to be configure in a way to support growth of emerging technologies that could help achieve market transformation.

6. Successful market transformation involves more than addressing economic barriers. Market barriers must be taken into account and alignment of state policies to promote DG and energy storage is very important. As one respondent pointed out, the correct alignment of policies that provides investors with faith in the market is ultimately what moves market transformation forward.

INTRODUCTION | 3-1

3 INTRODUCTION

3.1 Purpose and Objectives of this Study

The Self Generation Incentive Program (SGIP), established in 2001 to respond to California’s electricity

crisis of 2000-2001, and to help encourage development of distributed generation.1 Since its inception in

2001, the SGIP has provided incentives to a wide variety of distributed generation (DG) and other

distributed energy technologies including wind turbines, fuel cells, gas turbines, microturbines, internal

combustion (IC) engines, waste heat to power systems,2 advanced energy storage (AES) technologies, and

pressure reduction turbines.3

As California’s energy landscape changed over time, the goals and objectives of the SGIP evolved to

respond to the new needs and challenges. The SGIP’s goals now include lowering greenhouse gas (GHG)

emissions, reducing customer electricity purchases, improving electric system reliability, and supporting

market transformation of distributed energy resources (DER).4

A 2011 Cost-Effectiveness study5 provided a qualitative consideration of market transformation effects of

the SGIP. However, no other effort had been taken to examine the extent to which the SGIP had influenced

the growth and market transformation of California’s DG and energy storage markets.6

Overall Purpose of this Study

The overall purpose of this market transformation study is to determine the extent to which the SGIP is

transforming California’s DG and energy storage markets. Based on the findings, the study also contains

recommendations on ways in which the SGIP can help further support DG and energy storage market

transformation in California.

1 Senate Bill 970 (Ducheny), September 7, 2000 legislatively established the SGIP. CPUC Decision D.11-09-015, September 8,

2011 provided direction for implementing the SGIP. See http://docs.cpuc.ca.gov/PublishedDocs/WORD_PDF/FINAL_DECISION/6083.PDF

2 Waste heat to power is the process of capturing heat discarded by an existing industrial process and using that heat to

generate power. Waste heat to power technologies can include steam turbines, Organic Rankine Cycle (ORC) and the Kalina Cycle. Up through 2014, the only waste heat to power technology active within the SGIP has been ORC. Consequently, for this study, only ORC systems are treated as waste heat to power technologies.

3 Solar photovoltaic (PV) technologies were originally eligible under the SGIP. However, with emergence of the California

Solar Initiative (CSI) and in accordance with Assembly Bill (AB) 2778 (Lieber, September 2006), PV technologies were removed from the SGIP. Instead, incentives for PV technologies are provided under the CSI.



5 Itron on behalf of PG&E and the SGIP Working Group, “CPUC Self-Generation Incentive Program: Cost-Effectiveness of

Distributed Generation Technologies,” February 9, 2011.

6 Energy and Environmental Economics (E3) proposed a methodology in 2007 on evaluating market transformation effects of

the SGIP. The proposed methodology is similar to that used in this study but does not include market characterization or program theory and logic models. See Energy and Environmental Economics, Inc. “SGIP Market Transformation Effects Evaluation Methodology: Discussion Draft,” May 5, 2007 from http://www.cpuc.ca.gov/NR/rdonlyres/9462081A-B273-4014-B159-66E496107099/0/SGIPMarketTransformation5507.doc

Document title goes here

INTRODUCTION | 3-2

Specific Objectives

This study has the following seven objectives:

» Characterize the market for SGIP technologies in California

» Identify specific DG and energy storage market barriers and market strengths

» Identify and describe SGIP and other DG and energy storage policy interventions

» Assess the effects of the SGIP and other policy interventions in reducing identified market barriers and in supporting the transformation of the SGIP-eligible technology markets toward self-sufficiency

» Establish and analyze indicators, or metrics, that reflect the evolution of the DG and energy storage markets toward self-sufficiency

» Assess sustainability of these markets in California in the absence of the SGIP

» Identify and recommend changes to the SGIP program to support further development of these markets in California

3.2 Scope

The focus of this Market Transformation Study is to understand the influence of the SGIP on DG and

energy storage market transformation in California. As such, the timeframe for this study runs from the

start of the SGIP in 2001 through the program’s currently planned end date of 2020. Technologies being

examined include a subset of technologies eligible under the SGIP as of 2014, which includes wind

turbines, fuel cells, gas turbines, microturbines, IC engines7 and AES technologies.8 The study examines

renewable as well as nonrenewable energy resources. Solar PV technologies were originally an eligible

technology and make up a significant portion of California’s DER market. However, solar PV ceased being

an SGIP-eligible technology at the start of 2007 and is not examined in this study.9 In addition, the market

transformation effects associated with California’s solar PV market has been treated elsewhere.10

Focus on SGIP but Comparisons to Other DG Incentive Programs

While the focus on this study is on California, we also examine the growth of DG technologies in other

states with DG programs. We specifically examine programs active in Connecticut, Illinois, Massachusetts,

Michigan, New Jersey, New York, Pennsylvania and Wisconsin. By examining DG growth in other states

7 Fuel cells, microturbines, internal combustion engines, and gas turbines can be operated in either electric only mode or in a

combined heat and power configuration.

8 Although waste heat to power and pressure reduction turbines are technologies eligible under the SGIP, they are not

examined in this study due to the limited number of these projects that have been installed under the program. In particular, there were only 10 active WHP and PRT projects in the SGIP by the end of 2014, only two of which have received incentive payments.

9 Solar PV technologies were originally eligible under the SGIP. However, with emergence of the California Solar Initiative

(CSI) and in accordance with Assembly Bill 2778 (Lieber, September 2006), PV technologies were removed from the SGIP. Instead, incentives for PV technologies are provided under the CSI.

10 Navigant for the California Public Utilities Commission, “California Solar Initiative Market Transformation Study (Task 2),”

March 27, 2014.


INTRODUCTION | 3-3

and programs, our intent is twofold: 1) to isolate the influence of federal program interventions on DG

market transformation, and 2) compare and contrast effects of DG market transformation in California

against that occurring in other states. By comparing and contrasting DG market growth in California versus

other states, we can quantitatively ascribe the influence of the SGIP on transforming California’s DG

markets.11

3.3 Defining Distributed Energy Resources within the Context of the

SGIP

The California Public Utilities Commission (CPUC) decision in September of 2011 set forth the goal that

the SGIP should help transform California’s distributed energy resource (DER) markets. In that same

September 2011 decision, the CPUC established six guiding principles for the SGIP. Among the guiding

principles was the statement “the SGIP should support behind the meter ‘self-generation’ DER

technologies, which serve the primary purpose of offsetting some or all of a host-customer’s on-site

demand.”

DER technologies can include DG, energy storage, energy efficiency and demand response systems. In

accordance with the CPUC ruling and for the purposes of this study, we treat the combination of small-

scale (i.e., less than 5 MW in rated capacity)12 DG and energy storage technologies as the DERs that form

the focus of SGIP market transformation activities.

We specifically concentrate on the extent to which the SGIP has helped shape and influence the

transformation of DG and energy storage markets in California. In the rest of this section, we provide an

overview of DERs in California and examine the role of the SGIP in supporting market transformation of

DG and energy storage. We also define what we mean by market transformation and discuss the

complementary roles played by policies and programs in supporting market transformation. In the

following sections, we address the research questions to be answered with respect to characterizing

California’s DG and energy storage markets. These questions include the strengths and weaknesses of the

markets; the role of the SGIP and other policy interventions in helping transform the DG and energy

storage markets; the extent to which the SGIP has helped transform the markets; indicators that can be

used to mark the evolution of the DG and storage markets toward self-sufficiency and the ability of the

markets to be self-sustaining in the absence of the SGIP; and lastly, if there are modifications to the SGIP

to further support development of a sustainable California DG and energy storage markets.

3.4 California’s Programs for Distributed Energy Resources

California has a number of public purpose energy programs that address key energy issues facing the

state. The programs provide support to different energy technologies or measures as well as provide

support to different market segments. Support provided by public purpose programs range from

11 We initially intended to look at other states to see if we could isolate the influence of the SGIP on the growth of energy

storage. However, the energy storage market is still nascent and we were unable to find enough energy storage installation data in other states to be able to conduct a comparative analysis. As such, we limited our investigation to DG technologies.

12 In an effort to be responsive to the guiding principle focused on “behind the meter” applications, we selected a size limit of

5 MW as a reasonable upper limit of DG and storage technologies that could be located behind the meter.


INTRODUCTION | 3-4

assistance to low-income utility customers (for example, in the form of rate discounts under the California

Alternate Rates for Energy program) and energy efficiency and conservation programs targeting such

households to broader support for energy efficiency, renewable energy and DER programs.

Several programs were established in California to help support development of DG and renewable energy

technologies within the state. Examples of some of the earliest programs are the California Energy

Commission’s Emerging Renewables Program (ERP), the Biomass Demonstration Program and the Self

Generation Incentive Program. More recently established programs include the California Solar Initiative.

Assembly Bill 970 established the SGIP in 2001 to address peak electricity demand issues facing California’s

grid following the state’s electricity crises of 2000. The SGIP provides incentives to DG and combined heat

and power (CHP) systems located at primarily commercial or small industrial sites of utility customers.13

Together, the SGIP and the ERP provided complementary coverage14 of DG technologies to small and

medium-sized utility customers and helped provide a broader base of support for what would become

California’s growing DER market.

3.5 Overview of California’s Distributed Energy Resources

DERs make up a small but growing amount of customer-based power resources in California. At the end

of 2014, there were over 215,000 DER projects,15 responsible for over 2,500 MW of customer-based

generation.16

Table 3-1 is a summary listing of estimated DER projects and capacity located on the customer side of the

meter in California at the end of 2014.

13 The SGIP was established in response to Assembly Bill (AB) 970, which required the California Public Utilities Commission

(CPUC) to initiate certain load control and distributed generation program activities. The CPUC issued Decision 01-03-073 (D.01-03-073) on March 27, 2001 outlining provisions of a distributed generation program, which became the SGIP.

14 In general, the SGIP focused on nonresidential customers while the ERP focused on residential customers.

15 This number represents statewide DER projects; not just SGIP projects.

16 The SGIP has continued to grow. However, we use December 31, 2014 as a starting baseline in this study.


INTRODUCTION | 3-5

Table 3-1: DER Projects and Capacity in California at 12/31/14

PG&E SCE SDG&E POU17 STATEWIDE

Technology No. MW No. MW No. MW No. MW No. MW

Solar PV18 84,413 950.7 78,816 765.3 23,850 206.1 27,582 228.3 214,661 2,150.3

Wind 12 8.6 9 15.1 1 1.0 0 0.0 22 24.7

Renewable 66 25.8 42 25.7 16 8.5 8 4.9 132 65.0

--Gas Turbines 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

--Microturbines 15 2.1 6 2.5 4 0.8 0 0.0 25 5.4

--IC Engines 17 9.4 10 6.9 2 0.7 0 0.0 29 16.9

--Fuel Cells 34 14.4 26 16.3 10 7.1 8 4.9 78 42.7

Nonrenewables 255 102.8 206 109.5 54 28.6 37 18.7 552 259.6

--Gas Turbines 3 4.0 4 17.0 2 9.1 0 0.0 9 30.1

--Microturbines 49 10.8 52 8.9 13 1.1 9 2.2 123 23.0

--IC Engines 104 57.4 100 70.5 21 12.1 7 3.0 232 143.1

--Fuel Cells 99 30.6 50 13.1 18 6.2 21 13.4 188 63.4

AES 30 2.9 4 1.1 2 0.0 26 0.1 62 4.2

WHP 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

PRT 0 0.0 0 0.0 1 0.5 0 0.0 1 0.5

Totals 84,776 1,091 79,077 917 23,924 245 27,653 252 215,430 2,504

3.6 Overview of SGIP

California’s SGIP provides support to distributed generation and storage systems located on the customer

side of the electricity meter. Funded by California ratepayers, the SGIP is managed by Program

Administrators (PAs) representing California’s major investor owned utilities.19 The California Public

Utilities Commission (CPUC) provides program oversight.

Incentives are provided under the SGIP to participating customers to help offset the cost of buying and

installing SGIP technologies. By the end of 2014, the SGIP had provided over $530 million in incentives to

more than 769 projects representing more than 354 MW of rebated generating capacity. Consequently,

17 POU refers to publicly owned utility (e.g., Sacramento Municipal Utility District, Los Angeles Department of Water and Power,

etc.)

18 Solar PV estimates include completed projects from California Solar Initiative projects

(http://www.californiasolarstatistics.ca.gov/media/public_files/working_sets/WorkingDataSet_1-01-2014.zip); Single-Family Affordable Solar Homes (SASH) sites are apportioned to each IOU based on each IOU’s CSI general market share. Also included are Self-Generation Incentive Program (SGIP), New Solar Homes Partnership (NSHP) and Emerging Renewables Program (ERP) totals from SGIP impact reports and the CSI Go Solar database, and Publically Owned Utilities as reported to the California Energy Commission http://www.energy.ca.gov/sb1/pou_reports/Publicly_Owned_Utilities_Report_Summary_01-01-2013_to_12-31-2013.xls). Note these totals do NOT include systems installed outside of incentive programs since data for those systems is not yet readily available without NDA’s from the Investor Owned Utilities.

19 The Program Administrators are Pacific Gas & Electric (PG&E), Southern California Edison (SCE), Southern California Gas

Company (SCG) and the Center for Sustainable Energy (CSE), which administers the program in the San Diego Gas & Electric (SDG&E) service territory.


INTRODUCTION | 3-6

SGIP technologies make up approximately 14% of the state’s total DER capacity and approximately 16%

of the DER capacity located in IOU service territory.

3.7 Defining Market Transformation

Market transformation has been defined in a variety of ways. Within the context of the energy efficiency

market, the CPUC defined market transformation in a 2009 decision as follows:

Market transformation is long-lasting, sustainable changes in the structure or functioning of a

market achieved by reducing barriers to the adoption of energy efficiency measures to the point

where continuation of the same publicly-funded intervention is no longer appropriate in that

specific market. Market transformation includes promoting one set of efficient technologies,

processes or building design approaches until they are adopted into codes and standards (or

otherwise substantially adopted by the market), while also moving forward to bring the next

generation of even more efficient technologies, processes or design solutions to the market.20

More recently, Keating and others have defined market transformation as efforts…

…designed to induce sustained increases in the adoption and penetration of energy efficient

technologies and practices through structural changes in the market and in behaviors of market

actors.21

Schiller describes market transformation in a similar means as…

…long-lasting sustainable changes in the structure or functioning of a market achieved by reducing

barriers to the adoption of energy efficiency measures to the point where further publicly-funded

intervention is no longer appropriate.22

For this report, we adopt a modified definition of market transformation that targets DG and storage:

Long-lasting sustainable changes in the DG/storage market achieved by reducing barriers to the

adoption of DG/storage technologies and effecting structural changes in the DG/storage market

and in the behaviors of market actors such that the market can be sustained without publicly-

funded intervention.

It is important to note that market transformation goals have not been established for the SGIP. When

the program was established in 2001, it was in response to the electricity crises of 2000-2001.

Consequently, the SGIP was primarily established to help meet peak demand through the use of

distributed generation resources. It was only in 2011 that market transformation became a goal of the

SGIP.23

20 California Public Utilities Commission Decision D.09-09-047, September 24, 2009, Pg. 89

21 Keating, Ken, for the CPUC, “Guidance on Designing and Implementing Energy Efficiency Market Transformation Initiatives,”

October 13, 2014

22 Schiller, Steve, Presentation to the Kentucky Public Service Commission, “Market Level Evaluations,” September 11, 2009

23 In CPUC Decision 11-09-015 (September 8, 2011), market transformation became one of four goals for the SGIP. The four

goals are: 1) reduce GHG emissions in the electricity sector; 2) help with demand reduction and reduce customer electricity


INTRODUCTION | 3-7

Market Transformation versus Resource Acquisition

Programs like the SGIP are similar in nature to resource acquisition efforts typically undertaken in

integrated resource planning efforts by utilities. However, market transformation is significantly different

from resource acquisition efforts. Resource acquisition programs target participants to the program; look

to gain energy and cost savings primarily from program participants; focus almost exclusively on the

program; and, are controlled largely by the Program Administrators.24 In contrast, market transformation

targets all consumers; seeks to obtain energy and cost savings from the overall market; and is involved in

effecting changes in a dynamic market with changing market players, market forces and drivers. In

assessing the role of the SGIP in helping transform California’s DG and energy storage markets,

consideration must be given to barriers and opportunities that extend to the entire California DG and

energy storage markets.

Complementary Nature of Program Drivers, Policies and Market Players

in Market Transformation

Given that a single program by itself cannot transform a market, we need to examine what combinations

of actions lead to a transformed market. Market transformation programs are about strategic

partnerships with market players seeking similar goals for their own purpose. These market players are,

in turn, influenced by market conditions and policies. Market transformation occurs when there is a

convergence of market conditions, policies and market players aligned in a way that market barriers are

addressed effectively. In this study, we look at the policies, market conditions and the role of market

players within California’s DG and energy storage markets stretching from the start of the SGIP to 2014

with an eye to how these factors complemented or acted in opposition to one another. We overlay SGIP-

specific policies and actions on the market events as a way to help visualize SGIP’s role in these events.

3.8 Report Organization

This report is organized into seven sections and five appendices as described below:

» Section 1 provides an executive summary of the purpose, key findings and recommendations.

» Section 2 summarizes results from the report and addresses the primary goal and research objectives of the study.

» Section 3 is this introduction, which provides an overview of the SGIP and DER in California, defines market transformation, and identifies the scope, purpose and objectives of the study.

» Section 4 describes the approach in linking market transformation program logic and theory to past and current activities in California’s DG and storage markets, which includes characterizing California’s DG and energy storage markets, collecting information on market impacts and market players, and methods assessing the sustainability of California’s DG and energy storage markets.

purchases; 3) improve electricity system reliability through improved transmission and distribution system utilization and 4) help with market transformation of distributed energy resources.

24 Keating, op cite, page 4.


INTRODUCTION | 3-8

» Section 5 lays out the program logic and theory for assessing market transformation of the California DG and energy storage markets. The section presents how we use program theory to make a quantitative assessment of the impact of the SGIP on DG market transformation; including a discussion of statistical treatment to isolate the effect of SGIP.

» Section 6 characterizes California’s DG and energy storage markets including a substantive look at California’s markets by technology and market segment. Estimates include the technical, economic and market potential of DG and energy storage markets at different time periods. Market characterization also includes identification of important market trends taking into account market interventions at the federal and state level and SGIP-specific policies and DG support mechanisms.

» Section 7 presents a discussion on the extent to which the SGIP has influenced California’s DG market transformation. Indicators identified in Section 5 (program theory and logic) and statistical methods are used to quantify the possible influence of the SGIP on DG market transformation.

» Appendix A contains the survey forms prepared for collecting market information from DG and energy storage equipment manufacturers and project developers, other key industry stakeholders and PAs as well as over the phone and internet surveys with participants and nonparticipants.

» Appendix B consists of the aggregated host survey results resulting from the surveys conducted in the study.

» Appendix C provides a consolidated listing of the policies and regulations cited in the study as influencing California’s DG market transformation.

» Appendix D is a mapping of 2012 Census data NAICS codes using from 2-digit to 6-digit width to corresponding SitePro building types.

» Appendix E provides additional information on the approach used in developing CHP technical and economic potentials.

APPROACH | 4-1

4 APPROACH

This section describes the approach and methodology behind how we assess if California’s DG and

energy storage markets are being transformed and the role played by the SGIP in transforming the

markets.

We start by defining program theory and logic models. Program theory and logic models are used in

linking assumptions between causes of market barriers and expectations of how market interventions

will resolve the barriers to produce a transformed DG/energy storage market. The program theory and

logic model results are presented in Section 5.

We then characterize the California DG and energy storage markets using estimates of technical,

economic and market potentials. These estimates provide upper level estimates of the size of

California’s DG and energy storage markets by market segment and by IOU service territory. By

comparing estimated capacities of DG and energy storage installed in California, we are able to identify

the progress of market development. We also examine the growth of DG and energy storage capacity in

California in context of changes in policies or market conditions that may have influenced the growth of

these technologies. The technical, economic, and market potential results are presented in Section 6.

In order to understand the role of market interventions better, we also interviewed a number of key

players in the DG and energy storage markets as well as Program Administrators responsible for

implementing DG and energy storage incentive programs. We describe our approaches in collecting

information from these key stakeholders on their perceptions of California’s DG and energy storage

market challenges and opportunities and their views on the role of the SGIP in addressing these

challenges and opportunities. The collected interview data is used in developing the program theory and

logic model and the results are presented in Section 5. The survey forms and the aggregated host site

results are presented in Appendix A and B, respectively.

The primary focus of this study is to assess the degree to which the SGIP has influenced DER market

transformation.1 However, the SGIP is not the only force influencing market transformation of the

California DG market. To determine the impact on market transformation that can be assigned to the

SGIP, we use statistical means to isolate effects of the SGIP by comparing growth of DG and CHP

technologies in California to DG and CHP growth in other states and programs. We use the statistically

determined impact of the SGIP on California’s DG and CHP market growth to estimate what California’s

DG and CHP market growth might have looked like in the absence of the program. Results of the

statistical treatment are presented in Section 7.

1 As discussed in the introduction, DER is defined for the purposes of this study as DG and energy storage technologies. In

addition, we discovered early on in the study that there was insufficient data on energy storage technologies to provide a common database upon which to compare energy storage growth among different states. As such, our interstate comparison was limited to certain DG and CHP technologies.


APPROACH | 4-2

4.1 Program Theory and Logic Models

In deciding if a market is transformed, it is important to identify what the transformed market should

look like, how it should operate and how those expectations compare to the existing market. Strategies

for transforming the market are then based on addressing the barriers preventing such transformation.

Program theory and logic models are tools used in monitoring and evaluating a wide range of programs.2

Program theory describes the underlying assumptions about how a program is expected to operate.

Ideally, the theory provides a framework that describes the barriers preventing market growth and how

the program or interventions will result in intended outcomes. A logic model is a diagram that lays out

the key relationships between the program elements and the problems being addressed by the

program. The program logic model links desired or expected program outcomes to “who” delivers the

solution, “what” specifically is delivered, “when” it is delivered, and “how” it is delivered. Not only does

program logic help visualize the program theory but it also provides a means for measuring success of

the program in addressing the market barriers.

It is helpful to envision market transformation from the perspective of market growth and market

saturation. Figure 4-1 depicts the idealized growth of DG technology in California’s DG market starting at

2001 and continuing to 2020.

Figure 4-1: Linkages between DG Market Growth in California, Drivers and Barriers

2 Megdal and Associates, et. al. for the New York State Energy Research and Development Authority “Using program logic

model analysis to evaluate & better deliver what works,” European Council for an Energy Efficient Economy (ECEEE) 2005 Summer Study


APPROACH | 4-3

This concept is based on market diffusion theory.3 Over time, changes in market conditions, drivers

(including market interventions, such as SGIP incentives), barriers, perceptions of the technology by

market players and technology changes themselves determine the extent to which the technology

grows in the market. Ultimately, the policy goal is to develop a sustainable market that can survive and

thrive without market interventions. This goal not only requires growth in the DG and energy storage

markets but development of technologies and products that are cost-competitive without subsidies or

other market interventions.

A complication to assessing the influence of the SGIP on California’s DG and energy storage markets is

the absence of an existing program theory. The SGIP was not originally conceived as part of a market

theory targeting a sustainable DG/energy storage market. The SGIP was established in 2001 as one of a

collection of possible solutions to peak electricity problems facing California.4 Moreover, SGIP goals have

changed over time. Since its inception, SGIP program goals have been modified to help address key

energy issues. For example, legislation passed in 2003 required SGIP technologies to achieve low levels

of nitrogen oxides (NOx) emissions starting in 2007 to help establish ultra-clean distributed generation

technologies.5 SGIP’s goals now include lowering GHG emissions, reducing customer electricity

purchases, improving electric system reliability, and supporting market transformation of distributed

energy resources.6 A program theory and logic model developed now for the SGIP must necessarily take

into account these different expectations and goals.

The approach in developing a program theory for transforming California’s DG and energy storage

markets involves identifying the expectations, and identifying barriers that may prevent market

adoption of these technologies and achievement of the market transformation expectations. Examples

of assumptions about barriers or market failures include beliefs that DG and energy storage costs are

too high; that some policies may be preventing adoption of different DG or energy storage

technologies;7 that there is a lack of sufficient market players needed to create forward momentum in

the market; or, that there is consumer misperception about the performance or acceptability of DG and

energy storage technologies within their lifestyle or business mission. In addition, we establish

assumptions regarding drivers and opportunities that could propel forward DG and energy storage

technology adoption. Such assumptions could include beliefs that DG and energy storage technologies

are attractive to consumers as a means of having additional energy choices; that they represent a hedge

against possible volatility in energy costs; or represent a way for utilities to help achieve GHG emission

3 For example, see Michelfelder, R. and Morrin, M. “Overview of New Product Diffusion Sales Forecasting Models,” AUS

Consultants, from http://law.unh.edu/assets/images/uploads/pages/ipmanagement-new-product-diffusion-sales-forecasting-models.pdf

4 The Self-Generation Incentive Program (SGIP) was established in response to Assembly Bill (AB) 970, which required the

California Public Utilities Commission (CPUC) to initiate certain load control and distributed generation program activities. The CPUC issued Decision 01-03-073 (D.01-03-073) on March 27, 2001 outlining provisions of a distributed generation program, which became the SGIP.

5 Assembly Bill 1865 (Leno), February 21, 2003.



7 For example, AB 2778 enacted in September of 2006 limited SGIP eligibility to fuel cells and wind energy systems


APPROACH | 4-4

reduction goals more cost effectively than other options. In establishing assumptions regarding DG and

energy storage market barriers and opportunities, we rely on literature, legislation, and CPUC decisions

extending from inception of the SGIP up through current times. We also rely on interviews with current

and past SGIP Program Administrators, CPUC staff involved with administering the program, and key

stakeholders in California’s DER market.

Program theory also allows us to view the relationships between the desired outcomes and observed

outcomes. These distinctions are important when assigning changes in the market to different market

components.

Program logic provides linkages between specific program elements (e.g., outreach, incentives, etc.) and

expected outcomes that develop in the short-term, near-term, or long-term. Figure 4-2 shows a

simplified layout of program logic. The diagram also points out that each stage in the logic layout can be

used to explain the relationships between the different activities and expected outcomes.

Figure 4-2: Simplified Program Logic Layout8

In developing program logic diagrams, we take into account that the SGIP is not the only market

intervention affecting California’s DG and energy storage markets. However, when considering the role

of the SGIP on the markets, we examine the expected role of different elements within the SGIP such as

incentives, outreach, program rules, etc.

Outcomes can act as metrics toward market transformation. For example, one goal of DG and energy

storage market transformation is the emergence of technologies that are cost-competitive in the market

without incentives. We use changes in technology costs and cost-effectiveness tests as a way to

determine if market transformation is occurring. A robust and sustainable DG and energy storage

market also requires adequate delivery lines of products and services. Consequently, we use numbers

and business sizes of DG and energy storage technology suppliers as metrics for assessing growth in the

DG and energy storage supply infrastructure.

Timing of outcomes provides a means to measure progress toward market transformation. In particular,

market diffusion provides a way to measure the extent to which DG and energy storage technologies are

8 Braverman, M. “Theory and Rigor in Extension Program Evaluation Planning,” June 2009. From:

http://www.joe.org/joe/2009june/a1.php


APPROACH | 4-5

approaching market saturation at different times. We evaluate market penetration of DG and energy

storage technologies relative to their economic potential. Because market deployment in early stages of

market transformation may be slow, we use combinations of metrics to measure progress. Such

combinations of metrics can include interest in DG and energy storage technologies (manifested by

technology orders to suppliers or applications to DG and energy storage incentive programs), backlog of

orders, and increased investment in the DG and energy storage markets.

Program theory and logic supply the story line behind assumptions about market barriers and

expectations on ways we can use interventions to address the barriers. However, it is important to

ground assumptions and expectations with information on market characteristics and operation.

4.2 Characterizing California’s DG and Energy Storage Markets

The term “market characterization” is generally used to refer to a qualitative assessment of the

structure and functioning of a market, the primary purpose of which is to understand how the market

functions in order to be able to effect market transformation.9

We begin by developing estimates of the potential size of California’s DG and energy storage markets.

Estimates are developed based on the technical and economic potentials. This in turn allows assessment

of the changes in growth of California’s DG and energy storage markets by comparing the capacity of

deployed DG and energy storage technologies in the market to the overall potential size of the market.

In addition to characterizing the market in terms of size, we also describe DG and energy storage market

operations. Operations consist of how products and services are offered in the market, how value is

determined, and how different players in the market interact.

Technical, Economic, and Market Potentials

The first part of our approach in characterizing California’s DG and energy storage markets is to estimate

the potential size of these markets. This gives us an upper limit by which we can measure relative

growth in the DG and energy storage markets over time. We identify DG and energy storage markets by

technology and market segment. Because we focus on DG and energy storage technologies located on

the customer side of the meter, we discuss these technologies only in the residential, commercial and

small industrial segments. We also characterize the DG and energy storage markets at the end of 2014

because it represents close to current conditions and the projected future market at 2020; which

coincides with the expected end date of the SGIP.

The potential size of the DG and energy storage market can be defined based on increasing levels of

constraints as shown in Figure 4-3. Technical potential refers to the potential that is constrained only by

technical conditions such as performance or physical requirements. Technical potential is a useful

definition as it is links the total amount of potential installed capacity of market size to DG and energy

storage performance characteristics. For example, technical potential allows us to estimate the

maximum capacity of CHP technologies needed to meet the on-site thermal and electrical needs of

different classes of utility customers. Technical potential tends to overestimate market size because it

9 Ridge & Associates for Pacific Gas & Electric, “California Schools Market Characterization,” September 20, 2005, page 1-1


APPROACH | 4-6

ignores economic considerations. Economic potential takes into account the cost of different

technologies. In the case of the DG and energy storage market, we use cost-effectiveness test results to

estimate the capacity of different DG and energy storage technologies that can be deployed in different

market segments.10

In developing estimates of market potential, we used a hybrid Delphi approach. In particular, first order

estimates of market potential (as percentages of economic potential) were taken from other studies.

Using historical market data, we used logistic models to develop S-curves which provided us with annual

growth rates associated with achieving the first order market potential target. We examined the average

annual growth rates and assessed the reasonableness of the growth rate given the technology. If the

growth rate was considered unrealistic, the target market potential was readjusted and a new set of S-

curves generated.

Figure 4-3: Different Definitions of Potential11

10 We specifically use total resource cost (TRC) test results for DG and energy storage technologies from the SGIP cost

effectiveness study in estimating economic potential. This approach is consistent with the manner in which energy efficiency economic potential is typically estimated. See for example, the 2008 California energy efficiency potential study (http://www.cpuc.ca.gov/NR/rdonlyres/F8F8F799-40A8-4856-869F-713D6E6FF5E0/0/2008CaliforniaEnergyEfficiencyPotentialStudy.pdf) and the 2015 California energy efficiency potential study (http://www.cpuc.ca.gov/NR/rdonlyres/0C4CF052-0E02-4776-A69A-88C619AC8DFB/0/2015andBeyondPotentialandGoalsStudyStage1FinalReport92515.pdf)

11 From NREL, “U.S. Renewable Energy Technical Potential,” http://www.nrel.gov/gis/re_potential.html

http://www.cpuc.ca.gov/NR/rdonlyres/F8F8F799-40A8-4856-869F-713D6E6FF5E0/0/2008CaliforniaEnergyEfficiencyPotentialStudy.pdf

http://www.cpuc.ca.gov/NR/rdonlyres/F8F8F799-40A8-4856-869F-713D6E6FF5E0/0/2008CaliforniaEnergyEfficiencyPotentialStudy.pdf


APPROACH | 4-7

While we can examine the technical and economic potential markets by each individual DER technology,

it makes sense to group technologies that have similar performance characteristics or meet common

market needs. We group all CHP technologies together because they have similar fueling characteristics

(e.g., most are fueled by natural gas) but also because they are unique in providing both electrical and

thermal energy to customers. Conversely, we group biogas and wind technologies in their own groups

because their technical potential is based on the amount of available resources rather than the end use.

Technical Potential Estimates

Combined Heat and Power

To estimate CHP technical potential we establish basic engineering assumptions about the system

capacities and performances of commercially available IC engines and microturbine systems as

representative CHP systems. Performance characteristics include average electrical and overall system

efficiencies and useful heat recovery rates. We model performance of CHP systems to satisfy SGIP

requirements (i.e., assumed capacity factors and useful heat recovery rates that help achieve the GHG

emission rates associated with SGIP eligibility).12 We then model hourly electrical and thermal loads of a

building and identify the largest CHP system to reduce those loads while also having high usage and heat

recovery. We consider the basic heating needs that CHP might provide in the California climate for a

range of different commercial and residential building types and sizes. These needs are based upon

hourly models of heating and electrical end use needs across the state. Hourly heating and electrical end

use needs are based on data from the Commercial End Use Survey (CEUS) database13 and modeled using

Itron’s SitePro software.14 From these hourly models we identify a “preferable generator technology,” as

one which meets the performance requirements needed to meet both thermal loads and electrical loads

while simultaneously achieving GHG targets. We use the larger electrical capacity of the IC engine or

microturbine that meets these performance criteria for estimating technical potential. We estimate the

potential number of these generators based on 2012 Census counts of establishments by facility type,

size, and location based on postal service ZIP code. ZIP code spatial resolution of facility location allows

association to climate zone as well as to electric utility.

While census data provides us counts of different types of facilities, we also need to match facility type

with the facility types identified in the SitePro model. Consequently, we use specific North American

Industry Classification System (NAICS)15 codes to help match facilities identified in the census data with

facility types defined in SitePro. For example, many commercial and some manufacturing sector codes

12 Annual capacity factors assumed sufficient to recover all performance-based incentives. By GHG emission rates that match

SGIP eligibility requirements, we mean that CHP systems must achieve no higher than 379 kg/MWh of GHG emissions as specified in the 2014 SGIP Handbook, page 47.

13 Itron for the California Energy Commission, “California Commercial End-Use Survey,” CEC-400-2006-005, March 2006.

14 SitePro is a proprietary end-use load shape tool, based on national and regional energy use data and uses DOE 2 for the

simulation of HVAC energy. It can be used in conjunction with load research data or used independently to provide shapes for the residential, commercial, and industrial sectors.

15 NAICS codes represent the standard used by Federal statistical agencies in classifying business establishments for the

purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy.


APPROACH | 4-8

correspond to an “office” facility. We establish facility size using SitePro’s facility-specific reference

values of employment intensity. These values of square foot of facility per employee are multiplied by

midpoint values of Census employee count bins (e.g., 34.5 for 20 to 49 employees) to establish facility

sizes in terms of square feet of floor area. Different facility sizes are input to the consumption model. ZIP

code resolution of Census facility counts establish geospatial location and climate as well as electric

utility. This approach accounts for climate and size variables that strongly influence hourly energy use.

Using CHP system and facility parameters and our end use consumption model, we develop a CHP

system electrical capacity and specific prime mover type for each facility type, size, and climate. This

sizing approach is thermally driven, which means we size the CHP system to completely satisfy the

heating baseload while using all available waste heat from the CHP system. Where the resulting CHP

system thermal capacity results in excess onsite electricity, we reduce the electrical capacity. In this

case, the heating baseload will not be completely satisfied by the CHP system. However, in order to

optimize the project economics and maximize the rate of GHG emission reductions, we always set the

CHP size and operation to use whatever waste heat is available to meet the onsite thermal demand.

Renewable Resources

Technical potential of renewable resources (wind and biogas) are based on gross estimates of these

resources located throughout the state and then restricted by performance, geographical or land-use

constraints.

Wind technical potential is estimated using California wind resource data16 and wind turbine operating

characteristics (e.g., expected capacity factors at given wind speeds and turbine height). We restrict

wind applications to single site applications (i.e., wind projects are located at customer sites such that

they help offset the customer electricity demands) and turbine hub heights of no more than 80 meters

(approximately 260 feet). In addition, we limit wind applications using land use and environmental

restrictions developed by NREL.17 With hub heights of up to 80 meters, this leaves the possibility for

single wind turbine applications with reasonably high wind speeds that are still proximate to commercial

end users.

Biogas technical potential is based on numbers of landfills, wastewater treatment facilities and dairies

and estimated biogas production from these sources. For example, biogas potential for dairies is based

on the number of dairy cows and the estimated amount of methane produced from each dairy herd. We

take into account those dairies that have already installed dairy digester systems and generate

electricity. Similar treatment is applied to landfills in California.18 Biogas potential for wastewater

treatment plants is based on flow rates at each plant along with associated biogas production rates.

Larger landfills are required to employ landfill gas-to-energy operations. However, there are a significant

16 Sources of California wind resource data include CEC PIER wind resource data; NREL’s wind data site

(http://www.nrel.gov/gis/data_wind.html); and U.S. DOE EERE (http://energy.gov/eere/wind/wind-resource-assessment-and-characterization)

17 NREL, “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” July 2012, Appendix A

18 Sources of landfill gas information include EPA’s Landfill Methane Outreach Program (http://www.epa.gov/lmop/projects-

candidates/index.html); the California Biomass Collaborative (http://biomass.ucdavis.edu/); the California Biomass Energy Collaborative (http://www.calbiomass.org/renewable-energy/)

http://www.nrel.gov/gis/data_wind.html

http://energy.gov/eere/wind/wind-resource-assessment-and-characterization


http://www.epa.gov/lmop/projects-candidates/index.html

http://www.epa.gov/lmop/projects-candidates/index.html

http://biomass.ucdavis.edu/

http://www.calbiomass.org/renewable-energy/


APPROACH | 4-9

number of smaller landfills that are required by EPA to capture landfill gas but do not use the landfill gas

for energy purposes. In smaller landfills, the captured landfill gas is flared.

Energy Storage

The potential for on-site energy storage arises from opportunities to reduce demand during peak

periods by storing energy during off-peak periods. While energy storage will use more electricity to

deliver the same amount of electricity for end use, it will use less valuable electricity. The resulting

savings of on-peak demand charges can exceed the extra costs for additional electricity purchase,

installation, and operation of the energy storage system.

Energy storage is a suitable technology for various types of facilities. Energy storage systems that meet

large and critical needs will enjoy economies of scale besides providing other values like greater power

quality control. Technical potential is greatest where on-peak demands are high and differ greatly from

daily averages. Potential begins where on-peak loads exceed the daily average load.

To estimate technical potential we first establish basic engineering assumptions about the hours of

operation expected from an energy storage system. We then consider the greatest monthly on-peak

demand reductions the system could provide for a range of different facility types and sizes across

California’s climate. These on-peak reductions are based upon hourly-resolution models of facility

electrical demand across the state developed by Itron and based on the Commercial End Use Survey

(CEUS) data.19

From hourly demand models we determine power and energy capacities for storage. The technical

constraints are a power specification (kW) and an energy specification (kWh) sufficient to flatten the

highest four demand hours in a day to the fifth highest demand hour of that same day.

Charge is off-peak only and discharge is on-peak only. Off-peak demands increase accordingly and off-

peak demand peaks are allowed to increase. Charge is based on 100% round-trip efficiency. Discharge is

presumed to occur only during on-peak hours.

Using energy storage system and facility parameters and our hourly consumption model, we develop

electrical capacity specifications for each facility type, size, and climate. This sizing approach is driven

with aim to reducing the highest four demand hours each day.

DG and Energy Storage Market Dynamics and Major Market Players

Characterizing California’s DG and energy storage markets also involves understanding how the market

functions. Markets are more than collections of providers and consumers of products. Markets

represent dynamic financial environments wherein perceptions and market position can be as influential

as the price of a commodity. To characterize the DG and energy storage markets, we seek information

about what stimulates market growth and what causes market stagnation. We ask, “Who are the key

market players and what makes them critical players in the market?” “What drives sales of technologies

19 The Commercial End Use Survey (CEUS) is a comprehensive study of commercial sector energy use, primarily designed to

support the state's energy demand forecasting activities. The California Energy Commission maintains CEUS data. See http://www.energy.ca.gov/ceus/


APPROACH | 4-10

and services?” “How critical is market share and position relative to product innovation?” “How

important are synergies between policies that address market needs or barriers and the ability of

market players to act quickly to provide these products?”

We use in-depth surveys of key market players20 and stakeholders to gain an understanding of how the

DG and energy storage markets in California function.21 We conduct online surveys of utility customers

participating in DG and energy storage incentive programs or electing not to participate in these

programs to better understand underlying motivations in the market. By combining survey results, we

are able to put together a more complete picture of California’s DG and energy storage market. In

addition to primary research, we use literature related to California’s DG and energy storage markets,

data from technology manufacturers and project developers, policies and regulations that have affected

or are influencing the DG and energy storage markets and databases on DG and energy storage growth.

We use information on energy policies, regulations and market conditions to gain an understanding of

how these factors may have influenced actions by market players or stakeholders. We examine trends in

order to help evaluate if they had a sustaining effect on the DG and energy storage markets. We

examine trends within and outside of the SGIP.

4.3 Collecting Information on Market Impacts and Market Players

California’s DG and energy storage markets encompasses a wide variety of market players and

stakeholders who have differing roles.22 We used a combination of in-depth interviews and on line

surveys to obtain stakeholder perspectives on how the DG and energy storage markets function in

California. We also ask what have been and continue to be key barriers or opportunities, to what extent

the SGIP has influenced DG and energy storage market growth in California, and the survey participants’

thoughts on ways in which the SGIP can further help support DG and energy storage growth.

While there was no explicit intent to meet statistical significance, we based our sample sizes for all the

groups on a target of 90% confidence with 10% precision. However, we also assumed varying levels of

bias (50% - 75%) expected in the key question response depending on the group. Table 4-1 provides a

summary of the survey groups along with the population and sample estimates for each group.

20 Key market players are businesses or organizations that either conduct a significant volume of business in the market or

have a role in defining policies or regulations that can have significant impacts of market growth. Stakeholders are those entities that may have a vested interest in the market but are not necessarily key market players.

21 We consider market players to be companies and firms who are financially vested in the DG and storage markets. This

group consists of SGIP technology developers, project developers, service providers and utilities. We define stakeholders as organizations who have an active interest in the outcome of market development but may not be financially vested.

22 Among the groups we considered to be stakeholders included nonprofit organizations such as the American Wind Energy

Association (AWEA), the California Energy Storage Alliance (CESA), the California Clean Energy DG Coalition (CCDC) and the Clean Energy Coalition (CEA). Among the public institutions we considered as stakeholders included the California Air Resources Board (CARB), the California Environmental Protection Agency (Cal EPA) and the California Energy Commission (CEC).


APPROACH | 4-11

Table 4-1: Summary of Survey Groups

Group

Population

Estimate

Survey

Sample Description

Host Customers in California

SGIP Participants 712 Population estimate based on SGIP Tracking data of complete, in process or pending payment applications - Residential 54 27

- Nonresidential 658 48

SGIP Nonparticipants 1828 Population estimate based on SGIP Tracking data of incomplete, cancelled or withdrawn applications - Residential 635 48

- Nonresidential 1193 49

Manufacturers 141 38 Population estimate based on equipment details available in SGIP tracking data plus other known manufacturers

Project Developers/Installers

151 39 Population estimate based on installers from the SGIP tracking data plus other significant installers/developers operating in CA or outside

Stakeholders 10 Sample based on number of public agencies relevant to self-generation and industry representative groups.

Program Administrators 16 Sample based on combination of current and past SGIP Program Administrators as well as Program Administrators in NY, TX, MA and NJ

Participant and Nonparticipant Surveys

Participants are California IOU utility customers who applied to the SGIP, received incentives and

installed DG or energy storage equipment. We further break participants into market segments of

residential and nonresidential (i.e., commercial and small industrial). Nonparticipants are utility

customers that intended to install or installed an SGIP eligible technology at their site but did not go

through the program.

We developed a host customer survey to find out more about how utility customers view DG and energy

storage technologies, their level of experience with these technologies, reasons why they decided to

participate or not participate in the SGIP, their experience with the SGIP and any suggestions they may

have about improving the SGIP. The host customer survey was designed and fielded as an online

response form to over 1900 contacts available as part of the SGIP tracking data.

Manufacturer Surveys

Manufacturers are DG and energy storage equipment manufacturers whose equipment is eligible to

receive SGIP incentives or has been installed as part of an SGIP project. We included not only

manufacturers of equipment currently being installed under the SGIP but also manufacturers of DG

equipment installed in the early years of the program. The list of manufacturers was compiled based on

equipment information available in the SGIP tracking database. Due to the longevity of the SGIP, the list

of manufacturers who had been active in the program was fairly long. Consequently, we used a

shortened list to ensure there was a balance between DG technology representation by type of


APPROACH | 4-12

technology as well as timeframe of installations. Each manufacturer on the shortened sample list was

contacted individually for an in-depth interview.

The in-depth interview was designed to draw out information from manufacturers about the extent to

which they do business in California; the policies that they believe help support or act as barriers to

increased sales and deployment of their technology in California; the market conditions that they

believe either help support or suppress sales and deployment of their technology within the state; how

they believe their customers are responding to their technologies; as well as other key questions related

to the strengths and weaknesses of the DG and energy storage markets in California. We also asked

about their perception of the extent to which the SGIP is influencing DG and energy storage growth in

California.

Project Developer Surveys

We classify project developers as companies that develop DG and energy storage projects at host sites

and in many cases install the DG and energy storage technologies used in the projects. As with

manufacturers, we wanted to survey not just project developers currently involved with the

technologies but also reach back to some of the companies developing projects and installing DG

equipment in the earlier years of the SGIP. The list of project developers was compiled based on the

information available in the SGIP tracking database. The list was shortened to match the sample design

size, and to retain a balance between good representation of technology type and the timeframe of

project deployment. Each project developer who was placed on the shortened sample list was then

contacted individually for an in-depth interview.

The in-depth interview for project developers is similar in nature to the survey designed for

manufacturers. Like the manufacturing group, we wanted to find out how project developers perceive

the way in which the DG and energy storage markets function in California. However, while

manufacturers may not come into direct contact with utility customers, the project developers work

hand-in-hand with the customers. Consequently, the in-depth survey was designed to distinguish

between different geographical and market segments within California. We were particularly interested

in capturing insights from project developers about how they perceive drivers and barriers to DG and

energy storage deployment in California, if different DG and energy storage technologies face the same

drivers and barriers, if there are differences in perceptions among market segments, and how changes

in market conditions seem to be influencing market operations.

In some instances, companies who manufacture a DG or energy storage technology also act as project

developers. We noted these companies and developed a combined manufacturer and project developer

survey for conducting in-depth surveys for these companies. However, prior to conducting the

combined interview, we contacted the company to find out if the manufacturing and project

development activities were confined to one part of the company or operated in distinct and separate

parts of the company. In the latter case, we generally conducted separate in-depth interviews with the

two different operations within the company.23

23 In some instances, companies who provide both manufacturing and project development services requested that we

conduct only one combined survey even if operations were considered distinct parts of the company.


APPROACH | 4-13

Stakeholder Surveys

Stakeholders are people, groups or organizations that have an interest in the outcome of a venture.

Within the DG and energy storage markets, stakeholders include companies developing or deploying DG

and energy storage technologies as well as those companies or people purchasing or using these

technologies. However, stakeholders also include public agencies, environmental groups, the academic

community or nonprofit groups that have an interest in California’s DG and energy storage market

development. Our stakeholder sample included public agency representatives from the CPUC (both staff

involved in the SGIP and other DER related activities), the California Air Resources Board (CARB), the

California Environmental Protection Agency (Cal EPA) and the California Energy Commission (CEC).

Industry and nonprofit representative groups included the American Wind Energy Association (AWEA),

the California Energy Storage Alliance (CESA), the California Clean Energy DG Coalition (CCDC), and the

Clean Energy Coalition (CEA).

The in-depth interview for stakeholders sought perspectives on underlying drivers or barriers to DG and

energy storage market development in California. In the case of public energy and environmental

agencies, we wanted to learn background perspectives on the genesis of DG and energy storage support

programs and their relationship to energy and environmental policies in California. In particular, we

wanted to understand the extent to which DG and energy storage was considered to be a key ingredient

when the policies were formed and how that has developed over time. In instances where policies have

been implemented to manage DG and energy storage operation in the California market (e.g., air quality

control policies, nonbypassable fees, etc.), we sought perspectives on the reasoning behind the controls

and how this fit in with the expected role of DG and energy storage in California’s energy market. In the

case of industry groups, we sought their perspective on how California policies and regulations have

helped build or stymie DG and energy storage development in California over time, what progress has

been made in developing a sustainable DER market, what are the primary barriers and opportunities to

developing a sustainable California DG and energy storage market, and to what extent the SGIP has

helped develop that market in California.

PA Surveys

The Program Administrator (PA) group is a mix of both past and current SGIP Program Administrators.

The PA group provides unique insights into SGIP policies and goals, the functioning of the program and

intended outcomes as well as observed results. We expanded the PA group to include Program

Administrators of DG and energy storage support programs similar to SGIP but located outside of

California. These included DG and energy storage programs in New York, Texas, and Massachusetts. The

intent of the expanded PA group is to compare perspectives from California PAs to those of PAs with

programs in other states and see what commonalities and differences exist. We felt this comparison

could help us better understand basic DG and energy storage market operations that are independent of

geographical location.

We developed an in-depth PA survey that seeks the perspectives of the PA group to define the market

and future of DG and energy storage technologies.

Table 4-2 is a summary of the key research questions as addressed in the different surveys, along with

performance metrics or indicators collected via the surveys.


APPROACH | 4-14

Table 4-2: Key Research Topics

Research Topic

Performance

Metric/Indicator

Groups Being Surveyed

Seco

ndary

Data

Coll

ecti

on/

Lite

ratu

re R

evie

w

End U

ser/

Cust

om

ers

Inst

all

ers/

Dev

eloper

s

Manu

fact

ure

rs

Indust

ry G

rou

ps

PA

s +

PU

C

To characterize the market for SGIP technologies in California

CA Market segments for each tech # and MW

X X X X X

To identify distributed energy market barriers and market strengths

# Barriers X X X X X

# Strengths X X X X X

To identify and describe SGIP and other distributed energy resource policy interventions

Key policies related to DG that changed the market (5)

X X X X X

To assess the effects of the SGIP and other policy interventions in reducing identified market barriers and in supporting the transformation of the SGIP eligible technology markets toward self-sufficiency

Change in barriers and strengths due to policy

X X X X

To establish and analyze indicators, or metrics, that reflect the evolution of the distributed energy markets toward self-sufficiency

Change in production volume

X X

Change in number of Manufacturers

X X X

Change in number of installers

X X X

Emergence of new business models and financing options

X X X

Compare # of applications/sales to completes

X X X X

Diversity in installation market sector and geographic.

X X

To assess sustainability of these markets in California in the absence of the SGIP

Change in customer satisfaction

X X X X X

Change in customer awareness

X X X X X

Compare # installations with and without incentives

X X X X X

How important is cost in making the decision?

X X X X

To identify and recommend changes to the SGIP program to support further development of these markets in California

What changes would you want in policy and programs to support the market?

X X X X X X


APPROACH | 4-15

4.4 Evaluating SGIP Influences

In the previous section, we described our approach in characterizing California’s DG and energy storage

markets. We discussed ways to examine trends and interventions in the DG and energy storage markets

that may have acted to enhance or impede growth in the technologies. In this section, we look

specifically at the SGIP and our approach in evaluating the extent to which the SGIP has influenced

California’s DG market transformation.24 Results of the analysis are presented in Section 7.

We recognize that a number of factors influence the amount of DG that can be adopted into the market.

Consequently, we use linear regression analysis with multiple explanatory or independent variables to

isolate and estimate the impacts of the different factors.

We use a log-linear regression analyses to analyze the effects of different variables on CHP, wind, and

biogas capacity in California and eight other high priority states. By deliberate grouping of technologies

together into CHP, wind and biogas, we have developed sets of technology and their newly installed

capacity where there is overlap in the likely impact of some of the independent variables and lack of

variables in other cases. For example, air quality regulations may have a specific impact on CHP

technologies but a different and potentially opposite impact on wind or biogas to energy technologies.

However, all three groups of the technology groups are assumed likely to be positively influenced by

incentive programs.

In order to isolate the impacts of SGIP on newly installed capacity over time, we need to look not only

across different DG technologies but also different programs in different states that support DG. This

approach allows us to isolate the influence of SGIP policies on DG from the influence of other policies.

We use regression techniques to evaluate the influence of independent variables, including SGIP, on the

three groups of technologies. In general, this technique involves developing a yearly estimate of newly

installed capacity for each technology group, for each state, and each year of the analysis period. The

analysis team chose to focus on three technology groups (CHP, wind, and biogas fueled technologies

where the biogas was derived from landfill gas or dairy and swine digesters), nine states (California,

Connecticut, Massachusetts, Michigan, New Jersey, New York, Pennsylvania, and Wisconsin), and the

years 1999 through the most recently available data. The analysis process also required the analysis

team to develop information on the independent variables (policies and utility prices) that influenced

the installation of DG capacity over these states and time period. By looking at DG installations in

multiple states and controlling for DG programs and policies that support DG technologies, we can

isolate the influence of SGIP on newly installed capacity in California.

4.5 Assessing Market Trends Toward Self-Sufficiency

The statistical methods described above enable us to evaluate the influence of different factors, such as

policies and regulations, and to isolate the impact of SGIP-specific policies on new capacity in California.

By examining states that do not have DG support programs, the model can attempt to separately

24 Although we originally expected to examine the impact of the SGIP on the storage market, we found that there was very

limited data on storage capacity installed in other states. This is likely due to the still nascent stage of the storage energy market. Due to the lack of storage capacity data, we confined our analysis to the DG market.


APPROACH | 4-16

identify the impact of market-driven activities from regulations and programs designed to support the

installation of new DG capacity. A downside of this approach is that while we rely on historical market

data to estimate DG capacity in states with programs supporting DG, there is limited DG market data in

states that do not (or have not had) DG support programs. The low and highly variable level of newly

installed capacity may make it more difficult for the regression model to isolate the influence of

regulations and programs designed to support the installation of new DG capacity.

PROGRAM LOGIC AND THEORY | 5-1

5 PROGRAM LOGIC AND THEORY

This section provides a preliminary program theory and logic model for the SGIP. The program theory and

logic model identifies expectations about transformed DG and energy storage markets, about possible

interventions to address key market barriers preventing transformation of the DG and energy storage

markets and identifies activities that can be used by the SGIP to help with market transformation.

We base the model on interviews with California utility customers who installed SGIP technologies

(participants), customers who considered installing SGIP technologies but decided against it

(nonparticipants), SGIP technology manufacturers and installers, SGIP Program Administrators and

Program Administrators of DG programs similar to the SGIP and located in other states, CPUC staff, and

with other key players in the DG and energy storage markets. We also relied on secondary information

including white papers, reports or presentations from workshops where industry representatives or other

DG and energy storage market actors provided comments on DG and energy storage market barriers or

opportunities.

Because the SGIP has been active since 2001 our interviews include people who were active in the SGIP

in the past. Interviews with these people provide insights into how perspectives about the DG and energy

storage markets and the role of the SGIP have evolved.

5.1 Results

Based on interview results and secondary information, we developed a preliminary program theory and

logic diagram for the SGIP as shown in Figure 5-1.

The bottom of the diagram is broken down into short, medium and long term outcomes. We define short

term outcomes as outcomes that could be expected to occur in the next five years; prior to the current

planned end of the SGIP. We view medium term outcomes as occurring between the five and ten years.

Long term outcomes occur later than the next ten years. We selected these long term outcomes based on

deadlines of existing policy goals that might correspond to goals of transformed DG and energy storage

markets (e.g., AB 32 sets GHG goals to be achieved by 2030 and Governor Brown’s Clean Energy Jobs Plan

has goals to be achieved by 2020 and 2032).

The diagram also contains outputs and activities. Activities correspond to actions that could be taken

within the SGIP to help move towards transformed DG and energy storage markets. Outputs are the

products or services delivered by the SGIP as a result of the activities.

Sources of the outcomes, outputs and activities and additional explanation of their meaning and intent

are described in the sections that follow.



Figure 5-1: Preliminary SGIP Program Theory and Logic Diagram



Defining Transformed Markets

Long Term Outcomes

Long-term outcomes should ideally reflect the operation and structure of the transformed DG and energy

storage markets. While a number of policies have been enacted to help spur growth of DG and energy

storage in California, there has been little discussion of what transformed DG and energy storage markets

should look like or how they should operate. In addition, when the SGIP was established in 2001, market

transformation was not a goal of the program. Market transformation first showed up as one of four goals

for the SGIP in a 2011 CPUC decision.1

In a 2009 decision, the CPUC defined market transformation in context of the energy efficiency market:

Market transformation is long-lasting, sustainable changes in the structure or functioning of a

market achieved by reducing barriers to the adoption of energy efficiency measures to the point

where continuation of the same publicly-funded intervention is no longer appropriate in that

specific market. Market transformation includes promoting one set of efficient technologies,

processes or building design approaches until they are adopted into codes and standards (or

otherwise substantially adopted by the market), while also moving forward to bring the next

generation of even more efficient technologies, processes or design solutions to the market.2

Interviews with the SGIP PAs provide clear insights into some of the key barriers preventing increased

adoption of SGIP technologies into the market. When we combine PA perspectives about market barriers

with the CPUC’s 2009 definition of transformed markets, we believe there are several key elements that

can be used to characterize transformed DG and energy storage markets:

» There will be no significant barriers preventing utility customers and utilities to routinely use DG and energy storage technologies as part of their energy solutions;

» Changes in market operation along with performance and cost improvements will allow DG and energy storage to be adopted without incentives;

» The market will encourage development and adoption of even more efficient DG or energy storage technologies, services and solutions into the market; and

» The DG and energy storage markets will continue to operate and grow even if public interventions are modified, refocused or reduced.3

Transformed DG and energy storage markets should also provide nonenergy benefits to be sustainable.

By providing nonenergy benefits such as increased employment, cleaner air or water, and enhanced use

1 The CPUC Decision 11-09-015 (September 8, 2011) stated the four goals of the SGIP. Those goals are: 1) reduce GHG

emissions in the electricity sector; 2) help with demand reduction and reduce customer electricity purchases; 3) improve electricity system reliability through improved transmission and distribution system utilization; and 4) help with market transformation of distributed energy resources. See http://docs.cpuc.ca.gov/PublishedDocs/WORD_PDF/FINAL_DECISION/143459.PDF

2 D.09-09-047 at 89

3 This characterization comes from Ken Keating’s guidance to the CPUC on transforming the energy efficiency market. See

Keating, Ken, “Guidance on Designing and Implementing Energy Efficiency Market Transformation Initiatives,” October 13, 2014, page 2.



of local resources, DG and energy storage markets build broader acceptance of these technologies.

Existing policies supporting the growth of DG and energy storage growth in California include Governor

Brown’s Clean Energy Jobs Program, AB 32 (the California Global Warming Solutions Act of 2006), and AB

327 (which revises net energy metering provisions in California and requires utilities to file distributed

resources plans). Among the nonenergy benefits targeted in these policies include the following:

» Growth in environmental benefits including reductions in criteria air pollutants as well as greenhouse gas (GHG) emissions; and

» Creation of jobs and sustained clean energy employment.

In light of these considerations, we pose that long-term outcomes of transformed DG and energy storage

should include:

» DG and energy storage markets grow without incentives; and policies affecting the markets are aligned to help support continued growth;

» DG and energy storage make up a significant portion (e.g.,15%) of California’s electricity mix4;

» DG and energy storage help provide valuable environmental benefits that include reductions in criteria air pollutants, improved water quality and land use and reductions in GHG emissions; and

» DG and energy storage markets contribute to new job creation and sustained employment.

It would be valuable to have quantitative goals associated with these long term outcomes. Due to the

policy implications, goals should be adopted by policy makers who can take into account and balance the

different policies needed to achieve the goals.

Short and Medium Term Outcomes

Transformed markets emerge through sustained efforts that lead to long lasting changes in the market.

Short and medium term outcomes listed in Figure 5-1 support the development of long-term outcomes

and include the following:

» Outreach materials that identify how SGIP technologies match customer needs and utility mission;

» Development of a catalog of “prequalified” SGIP technologies and installation methods that are “proven,” highly replicable and represent certainty in costs and performance;

» Strategies that help move SGIP technologies to improved performance and cost targets while simultaneously stimulating growth in selected “high value” market segments;

» A coordinated approach with financial institutions to provide SGIP applicants with flexible, long-term financing vehicles that make adoption of SGIP technologies more attractive to a broader audience;

» Informational materials that help identify to policy makers how the SGIP is helping transform the DG and energy storage markets, where conflicting policies and regulations may be preventing that transformation and ways in which the policies could be aligned to achieve the desired policy objectives while growing the DG and energy storage markets.

4 Note that if DG makes up all of the 12,000 MW by 2020 goal of Governor’s Brown Clean Energy Jobs Plan, the 6500 MW goal

by 2032 for CHP reaches 3000 MW by 2020, and 200 MW of customer-sited storage is achieved by 2024, this would be represent over 30% of California’s peak demand in 2014 of 45,089 MW.



Interim outcomes are also important in creating milestones that can be measured to assess the progress

being made towards a transformed market.

Role of the SGIP in DG and Energy Storage Market Transformation

In his guidance document advising the CPUC on designing and implementing energy efficiency market

transformation initiatives, Keating points out differences between resource acquisition programs and

market transformation initiatives.5 A key distinction is the focus of the efforts. Resource acquisition

programs focus on achieving program level results. In contrast, market transformation initiatives focus on

producing changes in the market.

To date, the SGIP has acted primarily as a resource acquisition program. The scale of the SGIP has been

targeted at the SGIP program level rather than the entire DG and energy storage markets. For example,

the use of incentives targets program participants instead of a broader financial or support mechanism

that would target all end use customers. Similarly, the savings are estimated based on the program instead

of across the market. Lastly, the PAs largely control the pace and scale of the activities occurring in the

SGIP. However, in a market transformation initiative much of the how, when and where market impacts

occur requires the close coordination of a number of the key market players.

In identifying the role of the SGIP relative to a market transformation initiative, we have associated SGIP

activities and outputs to the short, medium and long term outcomes. We group SGIP activities and outputs

into six categories:

» Outreach: activities and outputs geared to increasing information about the SGIP and its relationship to DG and energy storage market goals

» Improved Cost and Performance: efforts to help move SGIP technologies to the point where financial incentives are no longer needed

» Improved Environmental Benefits: actions to expand and deepen the level of environmental benefits provided by SGIP technologies, thereby increasing the sustainable operation of the DG and energy storage markets

» Broader Financial Vehicles: efforts to increase the attractiveness of SGIP technologies to more end users who may otherwise lack the financial ability to adopt DG and energy storage technologies

» Aligned Policies and Regulations: actions to help align otherwise conflicting policies in order to develop sustainable DG and energy storage markets that simultaneously achieve energy, environmental and economic goals for California

» Policies Encouraging DG and Energy storage Market Growth: actions and products that pinpoint specific types of policies that can help grow DG and energy storage markets in California.

Figure 5-1 identifies specific activities and outputs that can be generated by the SGIP to help further DG

and energy storage market transformation.

The rest of this section describes the information collected from the surveys and that was used in

developing the program theory and logic model.

5 Keating, op. cit., page 14



5.2 Information Collected on Barriers and Opportunities

Program Associated Staff Interviews

These interviews included both past and present SGIP PAs, program EM&V staff and CPUC staff. We

interviewed all four current PAs. The intent of the surveys was to obtain perspectives on the barriers and

opportunities facing market transformation of DG and energy storage technologies and the role of the

SGIP. Selected staff associated with the program in the past were also interviewed to provide a historic

perspective on the SGIP as it evolved through the years since 2001. The interviews were all conducted as

structured in-depth conversations.

Program Goals and Expected Outcomes

The in-depth interviews were primarily aimed at finding out the PA and CPUC staff understanding of the

goals for the SGIP program as well as expected outcomes. All of the interviewed PAs stated that GHG

reduction was currently a primary goal of the program. They also mentioned that quantitative goals had

not been explicitly defined in the early years of the program. For example, the SGIP started primarily as a

load control and peak demand reduction program resulting from the energy crisis in 2000. The PAs

pointed out that while there was no explicit MW capacity goal; increasing the overall capacity of DG

technologies was an implied outcome that had for the most part met or exceeded expectations. However,

not having an explicit capacity goal was also cited as a problem for PAs in that it created difficulties in

making a clear assessment of the progress of the SGIP. One PA also pointed out that an implicit goal of

the SGIP was to have a diversified portfolio of technologies.

Increasing customer awareness of DG technologies was also not an explicit goal of the program. In one of

the initial decisions made by the CPUC in implementing the SGIP, the guidance was for program marketing

to be “conducted through existing networks of SPC program service providers.”6 The PAs felt it was an

implied outcome. The PAs also felt customer awareness had either been met or been exceeded through

increased outreach and other activities occurring in both the market and as a result of the program.

However, some of the PAs cited lack of marketing budget allocation in the administration funds as the

reason for no direct outreach activities. All outreach was leveraged through other existing channels and

thus limited in its attribution to the program. Customer satisfaction with technologies funded under the

SGIP, while an important component of market transformation, was not cited as an expected outcome.

Drivers and Barriers for DG and Energy Storage Technologies

Overall, incentives are perceived as a primary driver for promoting DG and energy storage technologies

under the SGIP. PAs also stated their perspective that increase in the efficiency of DG and energy storage

technologies along with the development of financing models available such as lease and PPA agreements

have helped drive the DG and energy storage market.

In contrast, PAs indicated that one of the biggest barriers to increased growth in DG and energy storage

is the lack of customer familiarity and comfortableness with the technologies. Most of the DG and energy

6 Decision 01-03-073, “Interim Opinion: Implementation of Public Utilities Code 399.15(b), Paragraphs 4-7; Load Control and

Distributed Generation Initiatives, March 27, 2001, page 29



storage are more complex than solar PV or involve more than “flipping a switch.” Consequently,

customers are not as comfortable or confident about the reliability of the technologies and do not adopt

them as readily as they adopt solar PV systems. The PAs also pointed out that CHP systems and fuel cells

require some level of engineering and proactive operations for customers to fully realize the benefits

associated with these technologies. In addition, because SGIP marketing is primarily conducted by project

developers, there is not an equitable approach to how and what customers are educated about the

benefits of SGIP technologies. In some instances, poor matches between customer needs and SGIP

technology costs or performance has led to less than optimal economics in spite of the available financing

mechanisms.

Some PAs expressed the view that steps to match customer needs and use cases to SGIP technologies is

one of the biggest untapped opportunities for growing the market. For example, CHP growth could

potentially be increased by identifying and marketing to those customers where there is heat load

concurrent to electricity loads or available waste heat from ancillary processes. The PAs also discussed

untapped market sectors, including the residential market for CHP and schools; and the small commercial

market for those DG and energy storage technologies that could provide demand response capabilities.

Specific technology barriers are addressed below.

Combined Heat and Power (CHP)

Some of the PAs viewed gas turbine technologies as a reliable source of power for very specific customers

and along specific demographic characteristics. Ideal applications are mostly large scale projects that have

large amounts of heat where waste heat utilization potential can be realized round the clock. The

economics of the technology also only typically works better for the larger customers. In addition,

financing rates are lower for large government entities in comparison to the private sector, making it more

practical and affordable.

In general, Air Quality Management District (AQMD) requirements and permitting requirements

represent significant barriers to further market adoption of CHP in California. Other technologies such as

fuel cells are exempt from these air quality permitting requirements due to their inherently low NOx

emissions. However, CHP systems have to go through an AQMD process which is viewed as being very

elaborate and restrictive. Most customers are reluctant to deal with the air quality permitting process. As

such, most CHP incentives are directed to government host customers who are more willing and likely to

go through the permitting process.

Another PA expressed the belief that a major barrier to CHP market growth is departing load charges. The

PA noted that departing load charges can represent a significant economic barrier for CHP and can

decrease benefits to host customers by 25%. Customers are billed based on their load, rather than the

energy they consume from grid. In addition, the PA noted that incentives provided to CHP technologies

are lower and considered to be less competitive than what is provided to other SGIP technologies. In

particular, the CHP incentive is based on an assumed capacity factor. The assumed capacity factor is

perceived as penalizing customers who may not have the load needed to match the required capacity

factor. If the host customers are not able to meet the performance based aspect of the incentive, they

may end up receiving only half the expected incentive. The mismatched base load and heat load make

this an issue for most customers, thereby making CHP look uneconomical. Also, CHP has more burden

when it comes to the GHG screening process and at least one PA indicated the SGIP uses an unrealistic



GHG reduction requirement for CHP systems. Lastly, CHP projects represents engineered systems and the

required amount of commissioning is more intensive and iterative than for simpler systems. The

commissioning process may go through many modifications before being finalized. As a result, CHP

systems can be viewed by customers and more challenging requiring a greater investment in time and

effort to participate in the SGIP.

Electric Only Fuel Cells

Several of the PAs identified the electric only fuel cell technology as one of the high growth technologies

in the SGIP. PAs noted that this growth was primarily driven by higher incentive levels, the presence of

motivated vendor(s) along with perceived affordability and reliability of the technology. In addition, at

least one PA noted that electric only fuel cells smaller than 1 MW in capacity were eligible for NEM, unlike

larger sized CHP technologies and increasing their financial attractiveness. PAs viewed higher upfront cost

investment as often being the major barrier; especially when there are no leasing or PPA models available.

Wind

Wind was cited as one of the low growth technologies served by the SGIP as the PAs found that there was

limited resource availability in their regions. Wind energy technology is perceived as having been around

for a while and is viewed as a known technology with good customer familiarity. The PAs perceive that

major barriers to wind systems are mostly the “not in my backyard” phenomenon which prohibits wind

turbine installations. In addition, PAs noted that wind is an intermittent resource that may have been

viewed by customers as lacking reliability. Also, the PAs perceive that wind turbines are not easily

adjustable to meet capacity needs of customers as they are available in fixed sizes and can only be installed

in multiples of that size.

Advanced Energy Storage

Since becoming eligible in the SGIP, advanced energy storage has seen significant growth in the SGIP. At

least one respondent indicated the main reason for growth in energy storage can be attributed to effective

marketing by vendor(s). The PAs perceive that the main attraction of energy storage to customers is the

potential to reduce demand charges coupled with smart controls. Even though the technology is

expensive, effective marketing has been able to increase adoption.

The main barrier to advanced energy storage is seen as regulatory uncertainty related to NEM and

interconnection of solar paired with energy storage. Some PAs expressed the opinion that the value

stream to AES will remain uncertain till these regulatory issues are resolved and the policy is clarified.

Influence of Policies and Regulations

We asked the PAs their perspectives on how different policies and regulations have affected the growth

of SGIP technologies. We also asked them to rank the policies with respect to how favorably or

unfavorably the policies have impacted SGIP technology growth.



Policies External to the SGIP

GHG and RPS policies:

For the most part, PAs rated GHG reduction requirements and RPS goals as either favorable or not having

much impact for most of the SGIP technologies. However, at least one PA noted the GHG requirement

that GHG emissions from SGIP technologies had to be 20% lower than the grid was impeding development

of gas fired CHP.

Air pollution permitting policies and regulations:

Almost all the PAs indicated that air pollution control permitting policies and regulations appeared to

impede growth of gas-fired CHP technologies. However, one PA noted that air permitting requirements

were forcing manufacturers to produce more efficient technologies with lower amounts of air pollution

emissions.

NEM policies:

While PAs indicated that the NEM requirements favored most technologies, at least one PA indicated that

existing NEM policies are unfavorable for large CHP. However, some of the PAs noted that because NEM

is not available to all SGIP technologies. Because NEM results in increased financial attractiveness and

helps to streamline project interconnection processes, NEM results in an “uneven” playing field among

SGIP technologies.

Utility interconnection policies:

PAs generally perceived the interconnection process as having no impact on SGIP technology growth. The

PAs indicated that most technologies have been through a learning curve with respect to interconnection

but are “now settling into the process”. However, at least one PA noted that advanced energy storage still

has unresolved interconnection issues which could impact growth of AES going forward. In addition, at

least one PA noted that municipal utilities have not been interconnecting gas fired CHP due to existing

renewable power contracts.

ZNE and LEED policies:

The PAs indicated that Zero Net Energy (ZNE) and Leadership in Energy and Environmental Design (LEED)

policies appeared to act favorably to SGIP technology growth or have no impact.

Tax treatment policies:

PAs viewed federal tax credit policies enacted through the Investment Tax Credit (ITC) and the Production

Tax Credit (PTC) along with the Modified Accelerated Cost-Recovery System (MACRS) as being very

favorable for growth of most SGIP technologies by helping to offset a big part of the technology cost. In

addition, the PAs indicated that loss of the ITC could unfavorably impact future growth of SGIP

technologies. Several PAs noted that not all SGIP technologies are eligible for federal tax credits.

Consequently, they felt that unequal treatment of tax credits created an uneven playing field for SGIP

technologies.



Policies Internal to the SGIP

Outreach policies:

The PAs had different opinions regarding the extent of outreach conducted by the SGIP and its impact on

SGIP technology growth. In general, the level and rigor of outreach varies across the PA territories. One

PA indicated that due to rapid oversubscription of SGIP funds, there was little need to conduct outreach.

In this case, the PAs primarily leveraged other outreach efforts. However, two PAs noted that they conduct

much more extensive outreach activities. One of the PAs expressed the opinion that even though they

were conducting more extensive outreach, there appeared to be little correlation between the amount

of outreach and the number of SGIP projects. That PA felt there was a need for more education. Another

PA perceived that their outreach efforts were extensive and was yielding more projects and more

informed customers.

SGIP incentives:

Almost all the PAs indicated that SGIP incentives were a primary factor in helping to grow SGIP

technologies. However, one PA noted that there are significant disparities in incentive levels between the

SGIP technologies and that technologies receiving lower incentive levels tended to grow at lower rates.

The PAs also noted hearing complaints from applicants about the different incentive levels. One PA also

pointed out that disparities in incentive levels was acting to reduce the diversity of technologies in the

SGIP.

Overall PA/CPUC Perspective and Key Takeaways

Nearly all of the PAs felt that there was a large untapped potential for SGIP to help grow the market for

SGIP technologies. Similarly, all expressed that there were a number of challenges involved in making

progress towards increased market growth but had different opinions about the types of challenges and

ways they could be addressed. For example, at least two of the PAs felt that utility tariffs could impede

growth of SGIP technologies by under-valuing the benefits provided by SGIP technologies. The PAs also

noted that customer perception that SGIP technologies are expensive or “risky” are significant barriers to

be overcome to achieve market growth. Additionally, some of the PAs also felt that SGIP funding allocation

to technologies was not equitable and unfairly advantaged some technologies, thereby impeding growth

of certain technologies.

Several of the PAs felt they were taking a more proactive approach in conducting outreach or marketing

technology offerings customized to create mutual benefit for both customers and the utility. However,

they noted that more could be done to educate customers as well as policy makers as to the benefits of

SGIP technologies.

Key takeaways from the interviews with the PAs and CPUC staff include the following:

» While market transformation was not a goal at the start of the SGIP, market development of SGIP technologies has been occurring under the SGIP;

» There is a significant untapped potential for SGIP to help grow the market for SGIP technologies;



» The lack of customer familiarity with SGIP technology costs and performance has created the perception among customers that SGIP technologies are costly and risky and poses a significant barrier to additional SGIP technology growth;

» It would be beneficial to educate customers and policy makers as to the costs, performance and benefits of SGIP technologies; and

» There is a need for alignment of critical state policies on DG and energy storage in order to make forward progress on market transformation. To be successful, the overarching policy goal of a transformed DG/energy storage market needs to be presented clearly and in a unified front. This unified front is what allows clean tech capital investors to have faith and invest in the innovative clean technology projects. Consistent alignment of clean tech policies with the subsequent capital investment from the tech capital investment community will ultimately be a key force in transforming the DG and energy storage markets.

Host Customer Interviews

Host customers for SGIP technologies in California were surveyed to get their perspective on market

barriers and opportunities for growing these technologies. The surveys included both customers that had

received SGIP incentives and those that did not receive incentives. In addition, the host customer surveys

spanned both residential and nonresidential market sectors. The surveys were structured to identify the

customer’s level of awareness of the SGIP technologies, gauge their satisfaction with the technologies,

obtain their perspectives on the market barriers and opportunities facing SGIP technologies and obtain

any recommendations they had related to changes in the SGIP or in policies affecting SGIP technologies.

The survey was implemented using an on-line format and thus the responses are self-reported. Within

the survey, customers were asked to select one of a number of responses that best fit their opinion.

However, customers were also provided an opportunity to provide open-ended responses to specific

questions. Surveys were emailed to over 1900 customers from the SGIP tracking dataset as one time

applicants with interest in program served technologies whether or not they went through with the actual

installation or received the incentive. Over 250 complete responses were analyzed to present the

following findings.

Technology Awareness and Satisfaction

The level of customer satisfaction with installed SGIP technology was used as indirect measure of the

opportunity for SGIP technology market growth. A high level of customer satisfaction with an SGIP

technology is likely to indicate the opportunity to grow the market for the technology. In general, SGIP

technologies involve complex operations that are difficult to market through conventional advertising. A

large base of mostly satisfied customers can play a significant role in increasing the adoption of

technologies which otherwise do not lend themselves to easy advertising. Consequently, word of mouth

and recommendations to friends and associates becomes an important aspect of growing the market for

SGIP technologies.

Figure 5-2 represents the summary findings from host customers regarding their satisfaction with the SGIP

type of technology installed at their site. Technologies examined included Advanced Energy Storage (AES),

Fuel Cells (FC), IC engines (ICE) and Microturbines (MT). Other technologies were included as “Other.”



Figure 5-2: Customer Satisfaction with Installed Technology

Customers were asked to rate their satisfaction with the technology as falling into one of the following

categories:

» Very satisfied (3)

» Satisfied (2)

» Somewhat satisfied (1)

» Not satisfied at all (0)

» NA (A third party or installer/vender took care of the application)

Note that responses include both customers who participated in the SGIP (“participants”) and received

incentives to install the technology as well as customers who installed a technology but did not participate

in the SGIP (“nonparticipants”).

The scale at the left-hand side of Figure 5-2 reflects the percentage of responses received from the

surveyed customers. Note the responses include a “Not Answered” response. Because these were online

surveys, if a question was left blank, we recorded the response as “Not Answered.”

The scale at the right represents the average response. The average response value is also represented in

each category.

Overall, customers expressed a high level of satisfaction with whatever was the SGIP technology they had

installed at their site. We also categorized responses by residential (“Res”) versus nonresidential

(“NonRes”) customers. In general, both residential and nonresidential customers appeared satisfied with

the technology they had installed.



Whether or not a customer would recommend the technology to friends and associates is another

measure of satisfaction with the technology. Customers were asked if they would recommend the

technology to their friends using the following responses:

» Very likely

» Not at all

A summary of the responses by technology and by customer segment (residential and nonresidential) is

shown in Figure 5-3.

Figure 5-3: Technology Recommendation to Friends and Associates

The scale at the left axis refers to the responses by percentage of customers. For example, 100% of

customers who installed an AES technology would recommend the technology to a friend or associate.

Note that because 10% of the customers answered “NA,” this was not counted as a percentage of the

recommendation response. Similarly, “Not Answered” was not counted as a percentage of the

recommendation response.

Overall, most of the surveyed customers indicated they would very likely recommend the installed SGIP

technology to a friend, indicating a high level of satisfaction with the technology. In addition, both

residential and nonresidential customers would recommend the technology to a friend or associate.

Drivers and Barriers to Adoption of SGIP Technologies

Customers consider a number of factors in deciding to install an SGIP technology. Factors that are

considered very important in the decision process are possible drivers to adoption of a technology. We

asked both customers who participated in the SGIP as well as customers who installed SGIP type

technologies but did not participate in the program to rank the importance of different factors that may

have influenced their decision to install the technology. The results are shown in Figure 5-4 below.



Figure 5-4: Host Customer Considerations for Installing Technologies

Customers were asked to rank the importance of five factors in their decision to install a technology. The

following are those five factors:

» first cost (First Cost);

» incentive level (Incentive);

» tax benefits (Tax);

» reliability of the technology (Reliability); and,

» operation and maintenance cost (OM Cost).

The customer choices for responses included:

» “Extremely Important:” given a score of 5;

» “Quite Important:” given a score of 4;

» “Moderately Important:” given a score of 3;

» “Slightly Important:” given a score of 2; and

» “Not At All Important,” given a score of 1.

We grouped the customer responses by whether they were participants (Part) or nonparticipants (NP).

Figure 5-4 shows the percentage responses for each of the participant and nonparticipant groups with

respect to the factor being considered (e.g., First Cost, Incentive, etc.). We also show a “Total Weighted



Average.” The Total Weighted Average represents the average response weighted by the capacity of the

technology installed by participants and nonparticipants.7

Reliability, and cost (both first and O&M costs) were found to be the predominant factors driving the

decisions for both participants and nonparticipants. Incentives and tax benefits appeared to be lower

considerations.

Perspective on the Role of the SGIP

A key objective of this market transformation study is to assess the importance that the SGIP may play in

moving the DG and energy storage markets forward. As a result, we felt it important to ask customers

how they viewed the role the SGIP made in their decision to install a technology. A significant measure of

the SGIP’s impact on customer’s intent to install the technology is related to the availability of SGIP

incentives. Consequently, we asked customers about their experience with the SGIP and specifically their

likelihood of installing the technology in the absence of SGIP incentives. Figure 5-5 below shows the

responses grouped by type of technology but also by market segment.

Figure 5-5: Installation of the Technology in the Absence of SGIP Incentives

The left axis of the chart shows the percentage of responses by the customers relative to the installed

technology. For example, for those customers who installed AES technology, approximately 50% would

7 Because both participants and nonparticipants in this study installed technologies, we were able to obtain the installed

capacities. We used the sum of the nonparticipant capacity and the sum of the participant capacity to develop a “total” capacity, which we then used to weight the responses.



not have installed the AES technology without SGIP funding. Conversely, less than 20% of customers

installing microturbines would not have installed the microturbines without SGIP funding.

To help make comparisons easier between the technologies, we developed a “probability” that customers

would install the technology in the absence of SGIP funding. The probability is calculated giving a 0 if the

customer answered “No, not at all” (they would not have installed the technology without the incentive),

0.75 or 75% for respondents that indicated that “Maybe, at a later time” they would have installed the

technology if there were no SGIP incentive, and 1.0 or 100% for respondents who stated that “Yes,

definitely” they would have installed the technology even if there were no SGIP incentive. The black dot

located in the center of each bar reflects the calculated probability that customers would have installed

the technology without the SGIP incentive. For example, we estimated the probability that customers

would have installed AES technology without the SGIP incentives at approximately 38%. Overall, most

customers would not have installed AES, fuel cells or IC engines without the advantage provided by SGIP

incentives. Nonresidential customers were also much less willing than residential customers to install

technologies without SGIP funding; which may be tied to the larger and therefore more capital intensive

projects installed by nonresidential customers.

We also wanted to assess customer satisfaction with different implementation aspects of the SGIP.

Consequently, we asked customers to rate their level of satisfaction of the SGIP with respect to the

application process (Application), the amount of incentives provided (Amount), the amount of time

associated with receiving the incentive (Time) and program requirements (Requirements). Results are

shown in Figure 5-6 below.

Figure 5-6: Satisfaction with Different Aspects of SGIP

In general, most participants are satisfied to very-satisfied with the SGIP application process, the amount

of the incentives provided, the time associated with receiving the incentive and other program

requirements. Even though nonparticipants did not receive incentives through the SGIP, they commented



on their level of satisfaction with all aspects of the program; some of which may have been tied to their

direct experience and some tied to their expectations.

Key Takeaways from Host Customer Surveys

Within the host customer survey, we asked customers for open-ended responses with respect to

recommended changes in the SGIP or to effect increased market transformation of SGIP technologies.

Among the more significant recommendations were the following:

» Most customers felt that high upfront costs represent a barrier to widespread adoption of DG and energy storage technologies. They felt it was important for the SGIP to help find ways to help reduce the cost of these technologies so incentives were not necessary.

» Customers also noted that most people are not familiar with SGIP technologies; their costs, their performance or their benefits. Consequently, in order to help grow the DG and energy storage markets they recommended that SGIP increase program outreach both with respect to SGIP technologies as well as various aspects of the SGIP itself.

» Customers also noted that there can be a great amount of discrepancy in sizing, costing and installing of SGIP technologies. Customers who noted this recommended that the SGIP help develop standardization in configurations and installations of SGIP technologies to help reduce costs and improve customer comfort level in adopting the technologies. There were also recommendations that the SGIP help screen or pre-qualify contractors who install technologies under the program to ensure high quality services. The expectation from host customers is that contractors associated with the SGIP need to provide independent and honest assessments; not sales pitches.

» A number of customers stressed that streamlining the incentive and utility processes are important in enabling higher adoption of SGIP technologies.

Manufacturer and Developer Interviews

We held in-depth interviews with companies who manufacture SGIP technologies and companies who

develop projects in California using DG and energy storage technologies. While most of the companies we

interviewed had been active in the SGIP some were aware of the program but had not been actively

involved in it. However, we felt it was important to obtain perspectives from both manufacturers and

project developers even if they had not been active in the SGIP but who could have an impact of future

DG and energy storage market development.

Advanced Energy Storage

Six different manufacturers and project developers were contacted to get their perspectives of the market

for AES and the SGIP’s impact on the AES market. We specifically wanted to find out what they viewed as

market barriers or market opportunities relative to growing the AES market in California.

While California was the primary market for these companies, they were all looking to or expanding

outside of California to locations such as Hawaii and even internationally (Australia and Japan).

Market sectors served by these companies included residential, hotel/lodging, offices, healthcare, retail,

academic, warehouses and the industrial sector. Most of the companies were targeting commercial and



industrial customers; however there were a few targeting multi-tenant properties. Most used power

purchase agreements (PPAs) or leases as the preferred mode of financing AES projects.

Market Barriers:

Most of the AES industry representatives interviewed reported that the utility interconnection process

was the most unfavorable policy aspect slowing market adoption of AES technology. NEM was also

pointed to as a market barrier as AES does not qualify for NEM; and representatives believe it creates a

market advantage for other technologies. These representatives also note that lack of customer

understanding of benefits provided by AES along with existing tariff structures were impediments to

having a broader group of customer adopt AES.

Market Opportunities:

The companies identified incentives as the main driver for the California market. At the time of the

interviews, California was seen as a large market because of the policy targets for installing AES through

IOUs as well as on the customer side of the meter. In addition, the capability of AES to reduce demand

charges was perceived by the AES representatives as one of the biggest drivers to host customers and

thereby increase adoption of energy storage.

Combined Heat and Power

Ten different manufacturers and project developers were interviewed for the CHP market. They

represented a combination of IC engine, microturbine, gas turbines and waste heat recovery technologies.

Many of the interviewed companies have been in operation for more than ten years and do business in a

variety of states other than California. Among the other states in which the businesses operate include

Texas, Pennsylvania, New Jersey, Connecticut, New York, Maryland and Arizona.

Market sectors served by the CHP industry include health care, lodging, retail, universities, warehouses

and the industrial sector. Many of the businesses use PPA agreements but a number also target larger

businesses that can provide their own financing. One representative noted that the “CHP industry has

predominantly done self-funding. Funding typically comes from businesses capital improvement

programs; which often is bond funding. Sometimes it is revenue funding from enterprise bonds.”

Market Barriers:

Almost all the CHP industry representatives identified departing load charges, standby and nonbypassable

charges as some of the biggest policy barriers facing increased CHP adoption. However, a number of the

representatives noted out that lack of customer awareness of CHP performance and benefits is an

important barrier. Several representatives pointed out that the long amount of time for permitting for air

quality reasons is also a significant barrier to increased market adoption. They also stated the belief that

emissions standards in California are too strict for small engines; and are impeding the development of

these smaller CHP applications.




A number of the CHP representatives (i.e., gas turbines, IC engines, microturbines) viewed their

technologies as being mature technologies with proven track record. They felt that because of these

characteristics, CHP technologies offered several things not available from other technologies including

increased reliability and resiliency, cost savings and the ability to reduce carbon emissions. Several

representatives also pointed out that with high electricity and low natural gas prices in California, there is

significant “spark spread” in the state; making CHP more cost effective than other competing

technologies. Some of the representatives pointed out that SGIP incentives have helped support for the

technology in the market.

Fuel Cells

Four different fuel cell manufacturers and project developers were interviewed to obtain their

perspectives on the fuel cell market in California.

Most of the companies interviewed have been in business for over 10 years. Geographically, most of the

companies indicated they targeted California because of the incentives but also were active in a number

of other states. Some of the companies were active internationally.

The target audience for fuel cells was much the same as for the CHP industry; with a heavy emphasis on

health care, lodging, retail, universities, warehouses and the industrial sector.

Market Barriers:

Representatives pinpointed high capital cost of the technology is one of the biggest barriers to widespread

adoption though they noted costs are continuing to come down. Two of the representatives highlighted

standby charges, departing load charges and the interconnection process as significant barriers. One

representative noted “When you have new building that was never connected to the grid, and you put in

CHP you still get hit even though you never were connected.” Because of the barriers, two of the

companies were beginning to focus their marketing effort back east due to California’s high standby and

departing load charges.

The lack of alignment in policy towards natural gas fuel cells was also cited as a significant barrier. The

representatives noted that the CEC views fuel cells as a preferred resource in the Integrated Energy Policy

Report (IEPR). However, SCE does not consider fuel cells in their Preferred Resources Pilot (PRP) program

and the CPUC’s Distributed Resource Plans (DRP) does not even require utilities to consider natural gas

fueled DG in their plans.


Representatives stated that the primary driver for the fuel cells is its attractiveness to customers as a clean

technology with high reliability. In addition, these representatives felt that the low to no emissions aspect

enables more rapid permitting; which provides fuel cells an edge over competing CHP technologies. They

also believe fuel cells can grow in the market once customers realize the benefits associated with the

combination of low emissions and small footprint with high power density. They also felt that increased

growth was possible if utilities realized the technology’s ability to provide grid support applications,

including providing reactive power.



Wind

Six different wind manufacturers and project developers were contacted obtain their perspectives on the

market barriers and opportunities facing wind technology.

The wind companies interviewed generally had been in business more than ten years. Their geographical

markets were generally focused in the United States. The targeted market sectors include residential,

agricultural, hotel and lodging, retail and the industrial sector.

Market Barriers:

The representatives noted that finding a match between the wind resource and customer demand is an

inherent challenge to the wind industry. In particular, one representative indicated they need customers

with at least 500 kW of constant load to make the economics work.

From a siting perspective, representatives stated the “not in my backyard” (NIMBY) attitude is preventing

increased adoption of wind. They also stated that lack of “green-friendly” tariffs in many locations was a

problem for expanded growth. Moreover, they felt some communities oppose wind installations; viewing

them as being obtrusive. Delays with interconnection and uncertainty about the ITC continue to be

problems. Representatives also pointed out that wind has a difficult time competing against other DG

technologies because wind doesn’t reduce demand charges. In addition, they felt that the wind industry

is undercapitalized as compared to the solar industry but felt that could be changing. One of the

representatives pointed out that California’s NEM policy is problematic in that it is limited to 1 MW; has

too many variations (making it difficult to understand and implement) and does not allow multiple

ownership of generation.


Most of the representatives viewed that wind’s 30 year track record gives customers confidence in the

technology. They also felt that wind’s lower cost per kWh is more competitive than most other

technologies. They also felt that policies such as AB 32 and the GHG reduction requirements are promoting

adoption of clean generation; including wind energy. They also felt that growth in complimenting

technology such as energy storage is helping bolster the value of wind energy. They viewed federal tax

credits (ITC/PTC) as being among the top drivers for increased market growth; and noted that when wind

is coupled with solar it is eligible for tax credit and depreciation. However, they noted the uncertainty

about tax credits is a problem.

At least one representative pointed out that wind growth opportunity exists if wind can be used as one of

the resources to help meet peak demand by allowing import of wind energy produced from states

adjacent to California. They pointed out that expansion of the balancing area to outside California for wind

can help flatten the high evening peaks shown in the CAISO “Duck Curve.”8 In addition, allowing export of

8 In examining possible impacts of increased intermittent renewable energy generation resulting from California’s Renewable

Portfolio Standard, the California Independent System Operator (CAISO) developed a net load shape curve that became known as the “Duck Curve.” The “Duck Curve” shows the possibility that high penetration of intermittent resources such as solar PV could significantly increase generation in the early afternoon (when demand is historically low) but then drop off steeply in the evening, when demand is typically highest. The early afternoon situation would constitute “over generation” issues whereas the steep drop off in generation in the evening would create a significant need for fast ramping generation.



wind power from California can also help avoid what is thought to be too high penetration; thereby

flattening the high afternoon generation part of the “Duck Curve.”

Key Takeaways from Manufacturer and Project Developer Interviews

» Delays and additional costs associated with utility interconnection is universally cited as a primary barrier to expanded adoption of SGIP technologies.

» The prospect of avoiding demand charges is viewed by the AES industry as one of the biggest value proposition for AES. However the lack of clarity in NEM policy is stranding the deployment.

» Departing load charges, standby charges and nonbypassable charges are the biggest barriers to CHP adoption as they significantly impact project economics and appear to the industry to be an unjustified cost.

» For fuel cells, their zero criteria pollutant emissions aspects, along with favorable footprint/power density provides them with a significant market opportunity, but requires additional education of customers. In addition, they believe these characteristics coupled with their ability to provide grid support applications including reactive power should make them attractive to utilities. However, high upfront costs remain a barrier.

» For wind the biggest barriers to increased wind growth are the difficulties associated with matching customer load with the availability of adequate wind resource; NIMBY attitudes and restrictions on NEM policies.

5.3 Barriers Facing SGIP Technologies

Table 5-1 is a summary of perceived or actual barriers to SGIP technology market growth. These perceived

or observed barriers are based on information obtained from interviews with a combination of

participants in the SGIP, project developers and manufacturers of DG and energy storage equipment and

key market players including SGIP PAs as well as PAs of different DG and energy storage incentive

programs located throughout the Unites States. This list is not comprehensive but meant to convey some

of the key barriers facing increased market growth of SGIP technologies.



Table 5-1: Summary of Barriers to SGIP Technology Market Adoption

Barriers Perceived Problem or Concern

High costs Some SGIP technologies can have high upfront costs, which makes them less attractive to potential users

Some DG technologies have fueling costs, which can fluctuate over time, making their costs uncertain

Costs of some SGIP technologies with slower learning curves, particularly those technologies that are already mature, will drop slower, making them less attractive future investments

Low value proposition

SGIP technologies with a narrow band of market application may be perceived as providing lower value in the market

Benefits from SGIP technologies may have important but hard to monetize benefits. Examples include increased reliability or resiliency to the grid and lower GHG or criteria air pollution emissions

Performance SGIP technologies with lower electrical efficiencies may be less attractive than higher efficiency alternatives

Some CHP with lower useful waste heat recovery rates can have lower GHG emission benefits and lower cost effectiveness

Some technologies with lower annual average capacity factors may provide less annual energy benefits, which may reduce their financial attractiveness or their ability to provide higher annual GHG emission reductions

Technologies with higher annual performance decay provide lower lifetime benefits

SGIP technologies with lower ramp rates have greater difficulty in providing load following capability and providing firming

Technologies using intermittent resources (e.g., solar and wind) cannot provide capacity on an as-needed basis without supplemental support (e.g., firming generation or storage)

Environmental impacts

Technologies with higher emission rates of criteria air pollutants may have higher capital and O&M costs (due to required air pollution control equipment); may have longer deployment time (due to permitting requirements) and may be perceived as “less green”

Technologies with higher GHG emissions are less attractive as a way to help meet GHG emission targets

Operation of technologies that impact wildlife (e.g., wind turbines and bats) or habitat may be more difficult to deploy and be perceived as being “less green”

Technologies that handle solid or liquid wastes (e.g., dairy biogas systems) may require permits, incur treatment costs and be viewed as being “less green”

Financing Some SGIP technologies may lack low cost financing that would help increase their attractiveness to potential users

There is a lack of third party ownership models in certain SGIP technologies that could otherwise help expand their market adoption

Financing institutes lack familiarity with certain SGIP technologies, making it more difficult for these technologies to receive low cost financing

Market awareness

Many potential users are unaware of SGIP technologies, their performance aspects and their potential benefits

Utility interactions

Interconnection requirements are still a problem for some SGIP technologies

Standby charges, demand charges, departing load charges pose economic challenges to increased growth of many SGIP technologies

Utility perspective that increased DG may make it more difficult to maintain system reliability and responsiveness

Resource availability

Some technologies require fuel resources (e.g., wind, waste heat, natural gas, biogas, etc.) that must often be contractually obligated over the life of the project

Some resources (e.g., wind, biogas) are distributed unevenly throughout the state; creating different value propositions for each utility

Institutional aspects

Lack of clear policies on NEM

Lack of cohesive and uniform approach on policies that affect future growth of SGIP technologies

Uncertainty on future and longevity of federal tax policies

Unfamiliarity of government policy makers with SGIP technologies and lack of connection to customer needs



5.4 Perceived Drivers to Growth in SGIP Technologies

Interviewees also identified a number of drivers they believe have either increased growth in SGIP

technologies or are important to future market growth of the technologies. Table 5-2 is a summary of

drivers as expressed by the different groups who were interviewed.

Table 5-2: Realized or Perceived Drivers to Increased Growth in SGIP Technologies

Surveyed Group Realized or Perceived Drivers

PAs (both inside and outside CA) and Program Staff

Incentives are viewed as the primary driver to expanding the program and promoting market development

Matching technologies to customer use cases is viewed as an “untapped” opportunity for expanding market growth

Flexible financing options are viewed as increasing the numbers and types of customers who can use the technologies and expanding market growth

Progressive environmental policies (e.g., GHG) can help drive “clean” DG and storage market growth

Alignment of policies can be a critical driver to increased market development of SGIP technologies by sending a positive signal to the investment market

Host Customers Low first costs influence the initial decision to participate; tax benefits were a secondary driver

Increased program outreach

Increased streamlining of the incentive process as well as processes that require utility approvals

Expanded number of technology vendors who provide honest assessments of technology costs and performance

Developers and Manufacturers

Overall government support of the technologies is viewed as critical in overcoming institutional and market barriers

Incentives or other financial support to help overcome first costs or market uncertainties

California’s need for “clean” and reliable DG technologies

Federal and state tax benefits

5.5 Expected Outcomes

Although helping to transform California’s DG and energy storage markets is one of four primary goals of

the SGIP, there has been little concerted discussion among policy makers, the SGIP PAs or stakeholders

about what would constitute transformed DG and energy storage markets.9

9 The four goals of the SGIP were stated in CPUC Decision 11-09-015 (September 8, 2011). The four goals are: 1) reduce GHG

emissions in the electricity sector; 2) help with demand reduction and reduce customer electricity purchases; 3) improve electricity system reliability through improved transmission and distribution system utilization and 4) help with market transformation of distributed energy resources. See http://docs.cpuc.ca.gov/PublishedDocs/WORD_PDF/FINAL_DECISION/143459.PDF



We can look to CPUC decisions to see if they help identify how the SGIP should help transform California’s

DG and energy storage markets. In its September 8, 2011 decision modifying the SGIP and implementing

Senate Bill 412, the CPUC adopted six guiding principles for the SGIP.10 Those six guiding principles are:

» The SGIP should only support technologies that produce fewer GHG emissions than they avoid from the grid.

» The SGIP should support behind the meter “self-generation” DER technologies, which serve the primary purpose of offsetting some or all of a host-customer’s on-site demand.

» The SGIP should only support commercially available technologies.

» The SGIP should target best of class DER by paying for performance.

» The SGIP incentives should focus on projects that efficiently utilize the existing transmission and distribution system.

» The SGIP should complement the structure of and be coordinated with existing ratepayer supported programs, especially the California Solar Initiative (CSI), which is aimed at transforming the market for renewable DG by driving down prices and increasing performance of DER.

These guiding principles help determine objectives of the SGIP moving forward and should be taken into

account when developing a SGIP market transformation plan. However, the principles constitute only a

portion of the expected outcomes of a transformed DG/energy storage market. We can use information

from the interviews to develop a more comprehensive but still preliminary list of expected outcomes.

Table 5-3 is a preliminary list of expected outcomes broken out by short term (e.g., less than five years),

medium term (e.g., five to ten years), and longer term (e.g., beyond ten years).

Table 5-3: Expected Outcomes by Timeframe

Timeframe Expected Outcomes

Short Term Increase interest in SGIP technologies

Improved performance and cost

Faster deployment of SGIP technologies

Improved ability to fit market needs

Increased number of market players

Improved ability to reach users

Medium Term Increased acceptance of SGIP technologies by customers and utilities

Significant improvement in SGIP technology cost and performance

Development of financing options that greatly expands market reach

Longer Term DG/storage makes up a significant portion of CA's energy mix

DG/storage provides valuable environmental benefits

DG/storage contributes to high employment and job development

10 Ibid

CHARACTERIZING CALIFORNIA’S DG AND ENERGY STORAGE MARKETS | 6-1

6 CHARACTERIZING CALIFORNIA’S DG AND ENERGY

STORAGE MARKETS

In characterizing California’s DG and energy storage markets, we provide estimates of the amount of DG

or energy storage capacity that could be installed in different market segments to meet the energy needs

of that market segment. For example, the “technical” DG potential represents the amount of DG capacity

that would serve the energy needs of the different applications ignoring the cost effectiveness of the

technology. The “economic” potential is a subset of the technical potential in that it identifies the amount

of capacity that could be installed not only to meet the energy needs of the market segment but also

meets specific energy cost targets. Estimates of technical and economic potential are further broken down

by specific DG technologies (e.g., fuel cells, IC engines, etc.) and by applications within a market segment

(e.g., hospitals, food industry, retail, etc.). We compare estimates of technical and economic potentials to

historical and projected capacities of DG and energy storage technologies in California. We then provide

estimates of “market” potential, which represents the amount of capacity we expect can be realistically

added to the market on an annual basis. Tracking market adoption rates against the technical or economic

potentials at different years identifies the extent to which a technology is making inroads into the market

segment, and ultimately towards a transformed market.

In addition, we overlay historical trends in the installed capacity of these technologies against the

backdrop of policies and market changes occurring during the observed timeframe. This overlay helps

pinpoint specific drivers that may have influenced technology adoption rates into the market.

In characterizing California’s DG markets, we break DG technologies into three broad technology

categories based on their distinct performance or fuel considerations: combined heat and power (CHP)

systems, wind energy systems, and biogas fueled technologies. Due to the small amount of installed

capacity of waste heat to power (WHP) and pressure reduction turbines (PRT) projects within the SGIP,

we do not include them in the market characterization.1 In addition, because of the emerging status of

energy storage technologies, we do not break the energy storage category down into any sub-technology

areas. Each of the following sections provides market characteristics by CHP, wind, biogas, and energy

storage categories.

6.1 California’s Combined Heat and Power (CHP) Market

In Section 6.1 we estimate potentials for new customer-sited CHP generating capacity in the service

territories of California’s investor-owned utilities (IOU).2 We begin by estimating total technical potential.

To identify new potential we subtract existing CHP capacity from the total. Next, we estimate the portion

of new technical potential that is economic. Finally, we estimate the portion of this economic potential

1 There is only one WHP project currently “active” in the SGIP queue, two PRT projects that are in the “PBI in payment” stage,

and seven other PRT projects are active in the queue.

2 Non-IOU utilities also have CHP potential. A 2012 report by ICF suggested 22% of remaining technical potential in 2030

would be among these utilities. About 60% of that is in the LADWP service territory.



that may be adopted as market potential. We forecast these potentials up to 2024 and distinguish them

by market segment, a CHP capacity bin, a climate region, and an electric utility.

Overall Approach

We use a “bottom-up” approach to estimate CHP potentials.3 We begin with counts of all business

establishments and residential units in the state in a base year. We then group them by common market

segment, estimate of building size, and climate. We model a group building by daily activity type and size

with a full year of hourly electrical and thermal end-use loads.4 We then develop an array of CHP system

specifications based on the modeled loads. The specifications include a prime mover technology, an

absorption chiller option, and a generating capacity.5 The largest of the generating capacities identifies

the group’s average technical potential and an associated prime mover technology and absorption chiller

option.6 Finally, we multiply a group average technical potential by its population to get a group total

technical potential.

To identify new technical potential we subtract existing CHP capacity from group total technical potential.

We apportion existing capacity to a group based on market segment, prime mover type, and capacity bin

proportions of total technical potential. The remainder is new technical potential by group. We forecast

future technical potential for groups using a base year estimate and segment–specific annual growth

rates.7

We determine economic potential based on total resource cost (TRC) tests that were conducted under a

complementary SGIP cost effectiveness study.8 To be consistent with the results of that study, we assume

that CHP systems that achieve TRC ratios of 0.8 or greater are cost effective. We examine the cost

effectiveness of over 70 proxy CHP systems to represent the many systems specified for over 500 different

groups. The proxy systems differ by prime mover type, capacity bin, climate region, and IOU.

If a group’s proxy system is cost effective in a particular year, the group’s economic potential equals its

technical potential in that year. If a group’s proxy system is not cost effective, we identify the next largest

capacity among its other CHP specifications that has a cost effective proxy system. That system’s capacity

is the basis of the group’s economic potential. We consider all the prime mover and absorption chiller

options before concluding a group has no economic potential.

3 A “top-down” approach might begin with total natural gas and electricity consumption by market segments and then

estimate what portions might be offset by CHP.

4 We use Itron’s SitePro software to model hourly loads by end-use based on market segment, building size, and climate

region.

5 Appendix E describes development of system specifications in detail.

6 Technical potential for CHP exists where onsite thermal loads can be served with waste heat captured from an electric

generator serving onsite electric loads. Thermal loads may include cooling loads where CHP system includes an absorption chiller.

7 Annual growth rates based on a 2012 ICF report: “Combined Heat and Power: Policy Analysis and 2011-2030 Market

Assessment,” prepared for the California Energy Commission, ICF International, Inc., February 2012. See Appendix E for list of assumed growth rates.

8 Itron on behalf of PG&E and the SGIP Working Group, “2015 Self-Generation Incentive Program Cost Effectiveness Study,”

October 2015; final report pending release



We also estimate market potentials using historical data on installed CHP capacity in California, logistic

models (S-curves) and a target level of market potential. We start by adopting a target market potential

from other previous potential studies. Using the logistic models and historical installation data, we

develop annual growth rates. We assess the reasonableness of the average annual growth and, if

necessary, readjust the target market potential. A wide variety of market barriers from alternative

investment strategies to uncertainty about air quality regulations, prevent some CHP with economic

potential from achieving market potential status. We take these factors, as well as the ability of the CHP

technology to respond to improvements in manufacturing, in assessing the reasonableness of the average

annual growth rate.

Estimated CHP Technical Potential

We estimate new CHP technical potential for over 500 groups categorized by market segment, building

size, climate region, and electric utility. Below we list 2014 technical potentials summarized by group

characteristics.

Technical Potential by Market Segment and IOU Service Territory

We estimate a 2014 new CHP technical potential of 24,236 MW across the service area of the three IOUs:

PG&E, SCE, and SDG&E. Table 6-1 lists new CHP technical potential subtotals in capacity (MW) for each

market segment as well as for each IOU. The market segments in Table 6-1 are primarily commercial apart

from large and small multifamily and food manufacturing segments.

Table 6-1: 2014 New CHP Technical Potential (MW) by Segment and IOU

Segment PG&E SCE SDG&E TOTAL Percent

College 54.7 106 12.79 174 0.72%

Food Manufacturing9 4,321 3,278 280 7,879 32.51%

Food Store 988 1,489 294 2,771 11.43%

Health, Hospital 1,120 1,237 222 2,579 10.64%

Large Multifamily 360 595 118 1,073 4.43%

Lodging, Hotel 644 634 204 1,482 6.11%

Office, Large 913 1,169 195 2,277 9.40%

Office, Small 52.2 62.38 18.38 133 0.55%

Restaurant, Sit-Down 829 1,737 390 2,955 12.19%

Retail, Large 539 833 179 1,551 6.40%

Small Multifamily 0.00 0.00 0.00 0.00 0.00%

School 5.22 5.58 1.49 12.3 0.05%

Warehouse 702 570 79.0 1,351 5.57%

TOTAL 10,527 11,717 1,992 24,236 100%

Percent 43% 48% 8% 100%

9 Food manufacturing includes food processing and handling



Table 6-1 shows SCE has over 11.7 GW and 48% of the total technical potential. PG&E has just over 10.5

GW and 43% of the total. In the smaller SDG&E territory, there is almost 2.0 GW of technical potential.

As identified in Table 6-1, the highest technical potential for CHP applications falls into five segments: food

manufacturing, the health and hospital industry, food stores (e.g., Safeway, Albertsons, etc.,) large offices,

and sit-down restaurants. Within these applications, CHP systems are typically used to offset electricity

demands (e.g., lighting or HVAC in offices, restaurants and hospitals, electric chillers and motors in food

stores, and food manufacturing) and to offset thermal needs (e.g., space heating in offices, domestic hot

water needs for laundries in hospitals and the health industry, cooling with absorption chillers in food

manufacturing and food stores). There are a variety of prime movers and configurations of heat recovery

systems for these different applications.10 Figure 6-1 shows how CHP systems can be used in these

applications to help meet electricity and thermal energy demands.

Figure 6-1: How CHP Displaces Host Site Electrical and Thermal Loads

10 Additional information on the different prime movers, their characteristics and the application of CHP to different market

segments can be found in the EPA report “Catalog of CHP Technologies.” United States Environmental Protection Agency. Combined Heat and Power Partnership, March 2015; from http://www3.epa.gov/chp/documents/catalog_chptech_full.pdf

CHP

Fuel C

Grid

Natural

Gas

Grid Grid Grid Grid

Natural

Gas

C

Natural

Gas

C

Natural

Gas

C

Natural

Gas

C

Natural

Gas

C

Boiler

Fuel

Baseline Boiler

Boiler Heat

Exchanger

Heating Load

Heat

Exchanger

CHP Prime Mover

Absorption

Chiller

Cooling Load

Electric

Chiller

Baseline Chiller

Grid

Electrical Load

Grid

Baseline Electricity

Prime Mover

Rejected Heat

BASELINE CHP



Table 6-1 shows the food manufacturing segment has the most technical potential with almost 7.9 GW,

one-third of total technical potential. This is the one manufacturing sector segment we included in the

study. Its potential is greatest for PG&E.

A roadmap that identifies the different pathways in which the food manufacturing and processing

industries can begin to approach the technical potential is beyond the scope of this report. However, due

to the significant technical potential associated with California’s food manufacturing industry, it is

important to have a better understanding of this industry. California’s food manufacturing/process

industry is highly diversified. It comprises over 3,000 plants processing commodities from over 88,000

farms. Over 240 commodity and trade associations represent food and agricultural interests in

California.11 The diversity of California’s agriculture across all sectors of food operations is reflected in the

range in size of the processing facilities. They include all types and sizes, from the “Mom and Pop” shops

to some of the largest single site operations in the world. In addition, the food manufacturing and

processing industries are closely associated with other market segments, which affects their overall

economics. Figure 6-2 shows the relationship and degree to which food manufacturing and processing are

interconnected to other market segments.

Figure 6-2: Relationship of Food Manufacturing and Processing Segments to Other Market Segments12

Water supply, energy supply, and sewage removal are essential to most food manufacturing and

processing facilities. These services, once inexpensive and taken for granted, have become expensive and

sometimes unreliable, placing California food processors at a serious disadvantage in the face of intense

11 Food Industry Advisory Committee and California Institute of Food and Agricultural Research University of California at

Davis, “California Food Processing Industry Technology Roadmap,” prepared for the California Energy Commission, July 2004. From http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.466.6676&rep=rep1&type=pdf

12 KMPG International, “The Agricultural and Food Value Chain: Entering the New Era of Cooperation,” May 2013. From

https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/agricultural-food-value-chain-report.pdf



competition from both domestic and foreign producers.13 Globalization of the food manufacturing and

processing industries is adding complexity to an already complex market.

Food manufacturing and processing companies in California contemplating CHP face a variety of

challenges including high upfront costs of CHP systems, internal competition for securing project financing

(versus use of capital for facility maintenance or expansion), concerns over potential project risks and

effects on project payback, perceived or real permitting and regulatory constraints, and concerns over

changing environmental or other regulatory policies.14

At the same time, CHP systems can provide real relief to food manufacturing and processing companies

facing increasing energy costs, concerns over future volatility in energy costs, and the need for highly

reliable power and resiliency. In addition, the ability of certain food manufacturing and processing

facilities to install biogas to energy CHP systems that use food processing wastes as a feedstock to an

anaerobic digestion process, can also help address waste disposal issues.

After food manufacturing, Table 6-1 shows a cluster of four segments each with between 9.4% and 12.2%

of technical potential. They include the two traditional CHP markets of health and office segments, and

two of the nontraditional CHP markets of restaurant and food store segments. All four segments have

between 2.5 and 3.0 GW of potential.

The three segments of retail, lodging, and warehousing each have about 6% of the total potential.

Technical potential falls below 5% with the large multifamily segment. College, school, small office, and

small multifamily segments together have 1.4% of total.

Note that Table 6-1 shows zero technical potential for the small multifamily segment. This segment

includes buildings with fewer than 50 units. These buildings are so small that technical potential for all of

them was less than the 30 kW threshold for technical potential.

Figure 6-3 shows segment percentages of IOU technical potential and MW of each IOU’s largest market

segment. The percentages differ between IOUs in part due to different concentrations and sizes of market

segments and in part due to different climates.

13 Food Industry Advisory Committee, ibid

14 Chittum, A. and Kaufman, N., American Council for an Energy-Efficient Economy (ACEEE), “Challenges Facing Combined Heat

and Power Today: A State-by-State Assessment,” September 2011. From http://aceee.org/sites/default/files/publications/researchreports/ie111.pdf



Figure 6-3: 2014 Technical Potential Percentages by Segment and IOU

Market Segments and CHP Capacity Bins

Technical potential differs not only between market segments, but also between capacity bins. Segments

with larger buildings typically require larger systems. We summarize 2014 technical potential by capacity

bins for the different market segments and each IOU.

Table 6-2, Table 6-3, and Table 6-4 show the breakout of 2014 CHP technical potential by market segment

and capacity for PG&E, SCE, and SDG&E respectively.

390 MW

3,278 MW

4,321 MW

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

School

Office, Small

College

Large Multifamily

Warehouse

Retail, Large

Lodging, Hotel

Office, Large

Health, Hospital

Food Store

Restaurant, Sit-Down

Food Manufacturing

Percent of New Technical Potential

PG&E

SCE

SDG&E



Table 6-2: PG&E 2014 Technical Potential by Segment and System Size Category (MW)

Segment

Under

50 kW

50 to

500 kW

500 to

1000 kW

1 to

5 MW TOTAL

College 1.02 14.3 4.29 35.1 54.7

Food Manufacturing 0.00 0.00 126 4195 4321

Food Store 0.00 148 369 471 988

Health, Hospital 0.00 120 388 612 1120

Large Multifamily 0.00 339 21.3 0.00 360

Lodging, Hotel 0.00 347 161 136 644

Office, Large 131 432 0.00 350 913

Office, Small 52.2 0.00 0.00 0.00 52.2

Restaurant, Sit-Down 276 553 0.00 0.00 829

Retail, Large 79.4 392 47.7 19.9 539

Small Multifamily 0.00 0.00 0.00 0.00 0.00

School 5.22 0.00 0.00 0.00 5.22

Warehouse 79.4 372 79.8 170 702

TOTAL 624 2,717 1,197 5,989 10,527

Table 6-3: SCE 2014 Technical Potential MW by Segment and System Size Category (MW)

Segment

Under

50 kW

50 to

500 kW

500 to

1000 kW

1 to

5 MW TOTAL

College 2.37 11.8 8.6 83.5 106

Food Manufacturing 0.00 0.00 107 3170 3278

Food Store 0.00 178 696 615 1489

Health, Hospital 0.00 94.8 473 670 1237


Lodging, Hotel 0.00 284 189 161 634

Office, Large 150 532 0.00 488 1169

Office, Small 62.4 0.00 0.00 0.00 62.4

Restaurant, Sit-Down 376 1361 0.00 0.00 1737

Retail, Large 0.00 701 93.1 38.8 833


School 5.58 0.00 0.00 0.00 5.6

Warehouse 58.1 295 114 104 570

TOTAL 654 4,052 1,680 5,330 11,717



Table 6-4: SDG&E 2014 Technical Potential MW by Segment and System Size Category (MW)

Segment Under

50 kW 50 to

500 kW 500 to

1000 kW 1 to

5 MW TOTAL

College 0.421 2.63 0.96 8.8 12.8

Food Manufacturing 0.00 0.00 10.8 269 280

Food Store 0.00 44.8 119 130 294

Health, Hospital 0.00 15.0 86.7 120 222


Lodging, Hotel 0.00 76.6 53.9 73.8 204

Office, Large 30.7 105 0.00 58.8 195

Office, Small 18.4 0.00 0.00 0.00 18.4

Restaurant, Sit-Down 79.6 310 0.00 0.00 390

Retail, Large 0.00 137 29.6 12.2 179


School 1.49 0.00 0.00 0.00 1.49

Warehouse 10.2 43.1 12.6 13.1 79.0

TOTAL 141 849 316.7 686 1,992

PG&E and SCE have the greatest technical potential in the 1 to 5 MW size category, with food

manufacturing dominating the category. SDG&E has greatest potential in the 50 to 500 kW category and

the second greatest potential in the 1 to 5 MW category. PG&E and SCE have the second greatest potential

in the 50 to 500 kW size category, with the restaurant and several other segments making up the greatest

potential. The third highest potential for all IOUs is the 500 to 1,000 kW category. Food store and health

segments dominate the 500 to 1,000 kW categories. All IOUs have least potential in the under 50 kW size

category, with the restaurant segment comprising the largest potential, followed by the office segment.

CHP technical potential will grow as new buildings are constructed. We estimated future technical

potentials using segment-specific cumulative growth rates. Table 6-5 lists the resulting current and future

year technical potentials.

Table 6-5: 2014 and Future CHP Technical Potentials by IOU (MW)

IOU

Technical

Potential 2014

Technical

Potential 2017

Technical

Potential 2020

Technical

Potential 2024

Technical

Potential 2030

PG&E 10,527 10,835 11,153 11,592 12,286

SCE 11,717 12,055 12,404 12,886 13,648

SDG&E 1,992 2,050 2,110 2,193 2,324

Total 24,236 24,940 25,666 26,671 28,258



CHP Economic Potential

We estimate CHP economic potential as the portion of new technical potential that meets a cost

effectiveness criterion. The criterion is based on a complementary study conducted on the cost

effectiveness of SGIP technologies. To be consistent with the results of that study, we assume that

technologies with a Total Resource Cost (TRC) benefit-to-cost ratio of 0.8 or greater represent cost

effective resources.15 The criterion is the same for every group and every specified CHP system regardless

of size or prime mover technology or inclusion of absorption chilling. System costs, on the other hand,

differ substantially between prime mover technologies. Emerging prime mover technologies like

microturbines and fuel cells currently have higher unit capital costs than IC engines and gas turbines.

Absorption chillers also add to system capital costs. System benefits also differ between inland and coastal

climate regions with inland regions showing a small advantage. System costs and benefits differ between

IOUs due to different electric and gas tariffs. Meanwhile, with time, all costs and benefits may change. As

more systems are sold, system capital costs tend to decline. As natural gas prices fall, system operating

costs decrease. These considerations were taken into account in the SGIP cost effectiveness study and

model16.

We use the SGIP cost effectiveness model to estimate benefit-to-cost ratios in TRC tests for current and

future years. We develop ratios for over 70 proxy systems distinguished by prime mover type, capacity

climate region, and IOU territory. Systems that met the criterion are included in economic potential for

the year. Figure 6-4 shows a select set of TRC test benefit-to-cost ratios from our cost effectiveness model.

The ratios are for proxy CHP systems located in the coastal climate region of PG&E territory. Ratios are

very similar in other inland and coastal climate regions and for other IOUs. Each series of vertical bars in

Figure 6-4 represents a proxy CHP system and shows its TRC benefit-to-cost ratios for years 2014, 2017,

2020, and 2024.17 Several of the system specifications include absorption chillers as indicated by the suffix

‘AbsChlr’.

15 For additional description of why a TRC ratio of 0.8 is used for establishing cost effectiveness and the details on the

approach, please see “2015 Self-Generation Incentive Program Cost Effectiveness Study,” October 2015; final report pending release

16 A publicly available cost effectiveness model entitled “SGIPce” accompanies the study and is pending release.

17 The cost effectiveness model does not extend to the year 2030.



Figure 6-4: Total Resource Cost Test Benefit/Cost Ratios for Select Proxy CHP Systems

Figure 6-4 shows that in each year fuel cells and microturbines have ratios above 0.6 but below 0.8.

Systems with these two emerging technology prime movers do not meet the cost effectiveness criterion

and so do not contribute to economic potential from 2014 through 2024. Figure 6-4 also shows that in

each year the other CHP systems have ratios above 0.8. Technical potential based upon these other

systems do contribute to economic potential from 2014 through 2024. Since none of the proxy systems

from 2014 to 2024 has TRC ratios that progress across the 0.8 criterion, we consider economic potential

in those years to arise solely from IC engine and gas turbine capacity.

New 2014 CHP technical potential includes 11.7 GW of fuel cells and microturbines that do not meet the

cost effectiveness criterion. As described earlier, however, for economic potential we do not consider

simply a group’s technical potential based upon its largest specified CHP system capacity and associated

prime mover. We also consider a reduced technical potential for the group based upon a smaller specified

system with a different prime mover. We then define a group’s economic potential based upon the largest

specified CHP system capacity that is cost effective.18

18 For example, a group’s CHP specifications may include a 500 kW fuel cell, a 400 kW microturbine, and a 300 kW IC engine.

Technical potential is 500 kW based on the fuel cell. Economic potential, however, is 300 kW based upon the IC engine because it is the largest capacity that is cost effective.

0.6

0.7

0.8

0.9

1.0

1.1

TRC

Ben

efit

/Co

st R

atio

2014 2017 2020 2024



For groups whose largest specified CHP system is a fuel cell or microturbine, we consider the smaller IC

engines and gas turbines specified for them as a downwardly adjusted technical potential. For those

groups, the smaller systems total 3.0 GW, or almost 25% of the 11.7 GW based upon fuel cells and

microturbines. As a result, 8.7 GW of new technical potential does not meet the cost effectiveness

criterion. Economic potential in 2014 then is 15.5 GW, or 64% of the 24.2 GW of 2014 new technical

potential. Since prime mover specifications for groups and prime mover TRC outcomes do not change

over the future years of interest, economic potential in those future years likewise is 64% of their technical

potential.

Potentials and Observed Growth in California’s CHP Market

California clearly has substantial CHP technical and economic potential in the 30 kW to 5000 kW system

capacity range. CHP in this range includes several different prime mover technologies. Figure 6-5 shows

observed CA growth since 1998 in cumulative CHP capacity for the different prime mover technologies

SGIP supported.

Figure 6-5: Cumulative CHP Installed Capacity in CA for Select Prime Mover Technologies (1998-2014)19

19 Source: DOE ICF data for CA, includes natural gas and biogas fueled CHP capacity

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Cu

mu

lati

ve In

stal

led

Cap

acit

y (G

W)

IC Engine Gas Turbine Fuel Cell Microturbine



Figure 6-5 shows that by 2014 almost 0.45 GW of cumulative capacity has been installed among the prime

mover technologies SGIP supported. During this period, the SGIP supported 0.33 GW of this new capacity.

Cumulative growth peaked around 2005 and slowed until 2012. Growth sped up through 2013 then

slowed again.

IC engines have been the predominant prime mover technology. IC engines currently contribute over

three times the capacity of the next largest contributor, gas turbines. Fuel cell and microturbine

technologies contribute smaller capacities. Annual additions of these two emerging technologies have

grown in proportion to IC engines. Additions, in general, are unsteady. Figure 6-6 shows annual additions

by prime mover technology.

Figure 6-6: Annual Capacity Additions in CA for Select Prime Mover Technologies

Figure 6-6 shows that in recent years, annual additions of IC engines have declined sharply both in

absolute and relative terms. Additions among the other technologies have cycled up and down. In 2012

and 2013, fuel cell and gas turbine technology contributions peaked respectively. Both then fell back

sharply but the peaks suggest greater rates of adoption exist.

0

5

10

15

20

25

30

35

40

45

50

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

An

nu

al C

apac

ity

dd

itio

n (

MW

)

IC Engine Gas Turbine Fuel Cell Microturbine



Technical Potential of All-electric Fuel Cells

When specifying CHP technical potentials for population groups we develop system sizes for several prime

mover technologies. One of these (electric only fuel cells) does not fall into the typical CHP configuration

since it does not recover waste heat for onsite use. Instead, heat from the process is captured and used

in the internal reformers to help increase the electrical efficiency of the fuel cell. The all-electric fuel cell

is recognized in the SGIP for its higher electric conversion efficiency despite not using waste heat to serve

thermal loads. Here we discuss the special treatment of all-electric fuel cells.

Most population groups have substantial periods with low thermal loads that sharply limit system capacity

specification. A large system with low thermal loads can waste heat. When we size a system as an all-

electric fuel, thermal loads do not matter. There is no constraint against wasting heat. Only electric loads

matter. This tends to bias all-electric fuel cell technical potential upwards relative to true CHP systems.

We attempt to address the upward bias of technical potential for all-electric fuel cells by recognizing an

operational reality of fuel cells generally. We typically see fuel cell operation with 365/24/7 generation

very near its ideal of full capacity, and with infrequent and small modulations in output. While both all-

electric and CHP fuel cells operate in this fashion, and many gas turbines too, we tighten an annual

operational constraint for the all-electric fuel cell only. The constraint is operation with a minimum of

7,000 equivalent full load hours of operation rather 5,000 used for CHP technologies. So, while only hourly

electric and not thermal loads matter for all-electric fuel cells, many more of those hours matter.

All-electric fuel cells contribute 4.3% to new technical potential. CHP fuel cells contribute 41%. Fuel cells

are a preferred prime mover technology where there is a high ratio of power to heat. With both fuel cell

types subject to the same 5,000 hour constraint, fuel cell contributions would change little but more

would be all-electric and fewer would be CHP.

With neither type of fuel cell meeting the cost effectiveness criterion during 2014 to 2024, they do not

contribute to economic potential during that time. This does not mean fuel cells will not be adopted during

that period. The continued adoption of this emerging technology, however, may require continued policy

support.

CHP Market Potential

CHP market potential is new CHP that actually may be adopted over time. As a starting point, we assume

market potential to be 25% of economic potential.20 Based on our cost effectiveness examination, 64% of

technical potential is economic in 2014 and that percentage will not change in 2024. In absolute terms,

new technical and economic potential both continue to grow. When considering market potential and

these ‘moving targets’, we set our sights on a fixed year of 2024. We then examine growth of cumulative

CHP capacity from its existing 2014 capacity to its 2024 market potential. The 2024 market potential is 4.5

GW. Figure 6-7 shows growth in existing and forecast cumulative capacity.

20 Adoption is not instant, taking from years to decades. The 2012 ICF “Combined Heat and Power: Policy Analysis and 2011-

2030 Market Assessment” describes adoption rates for base, medium, and high scenario forecasts where higher scenarios include more policy support. Adoption rates are 11.8%, 22.7%, and 38.3% respectively for base, medium, and high. These percentages are based upon new technical potential. When adjusted for 64% economic potential they are 18.4%, 35.5%, and 59.8%.



Figure 6-7: Growth in Cumulative CHP Capacity toward 2024 Market Potential

Figure 6-7 shows forecast growth in cumulative capacity based on actual history from before 1980 up until

2014. We use market saturation curves (known as sigmoid or “S-Curves”) in forecasting annual market

growth in CHP.21 The S-Curve growth model is a logistic model frequently used to forecast the growth of

a technology. The initial growth forecast is approximately exponential until almost 2030. Then as market

saturation begins, the growth slows and more gradually reaches the 2024 market potential. Figure 6-7

shows it is not until after 2060 that growth reaches the 2024 market potential. Although the market

potential will be greater in 2060 than in 2024, the forecast demonstrates the steep growth that would be

required to reach market potential.

Figure 6-7 includes two important policy targets based on new CHP capacity additions: the AB32 target

for 2020 and the Clean Energy Jobs Plan (CEJP) target for 2030.22 The growth forecast undershoots the

AB32 target slightly but then surpasses the CEJP by almost 500 MW, or 19% of the CEJP target. Slower

21 See for example, Michelfelder, R. and Morrin, M. “Overview of New Product Diffusion Sales Forecasting Models,” AUS

Consultants, from http://law.unh.edu/assets/images/uploads/pages/ipmanagement-new-product-diffusion-sales-forecasting-models.pdf

22 Both policy’s grand total targets include CHP capacity beyond the maximum 5000 kW capacity considered here. We take

only a portion of the grand totals in order to represent the limited range of 30 kW to 5000 kW discussed here.

2014644 MW

20201,725 MW

20303,086 MW

Clean Energy Jobs Plan Target2,594 MW

AB32 Target1,844 MW

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

1980 1990 2000 2010 2020 2030 2040 2050 2060 2070

Cu

mu

lati

ve C

apac

ity

(MW

)

Existing Forecast

2024 Market Potential 4,521 MW



growth may hit the CEJP target but would do so at the expense of missing the AB32 target. To match the

growth forecast of Figure 6-7 will require considerable acceleration in CHP adoption. Figure 6-8

demonstrates this acceleration in terms of both cumulative capacity growth and annual capacity

additions.

Figure 6-8: Growth in Cumulative CHP Capacity and Annual Capacity Additions

The lines and left vertical axis of Figure 6-8 show growth of cumulative CHP capacity in CA in the 30 kW to

5000 kW range. The red line shows historic growth that includes all existing capacity. The dashed yellow

line shows a growth forecast that adds new capacity. The forecast annual increase is an average of 13.5%.

This is the growth rate required for CHP to go beyond the 2014 cumulative capacity of 644 MW to the

forecast 4,500 MW capacity at 2024 on the logistic curve of Figure 6-7. This cumulative growth rate is

large but not unprecedented with energy technologies.23 Such rates sustained for a decade suggest a

substantial shift in energy market investment and focus.

23 For example, the solar PV market in the Unites States grew from 2009 through 2014 at a compound annual growth rate of

60%. See http://www.greentechmedia.com/articles/read/shayle-kanns-solar-predictions. We recognize that one of the factors allowing rapid PV growth is the capability to mass manufacture PV modules and other components and this may not be the case for CHP technologies. However, for smaller CHP systems, modular sizing may make mass production similar to PV more practical.

20242,279 MW

0

25

50

75

100

125

150

0

500

1,000

1,500

2,000

2,500

3,000

An

nu

al C

apac

ity

Ad

dit

ion

(M

W)

Cu

mu

lati

ve C

apac

ity

(MW

)

Existing Forecast

http://www.greentechmedia.com/articles/read/shayle-kanns-solar-predictions



The vertical bars and right vertical axis of Figure 6-8 show annual capacity additions in CA in the 30 kW to

5000 kW range. The red bars show existing historic additions that include capacity added during the SGIP

years 2000 through 2014. The yellow bars show capacity additions forecast that are new capacity. These

additions follow the logistic curve of Figure 6-7 to hit the 2024 market potential target after 2060. The

yellow bars indicate that new additions will be twice as large by 2020 as seen during the SGIP years. With

these additions, by 2024 the cumulative capacity reaches 2,279 MW. While this is still below the assumed

2024 market potential, as Figure 6-4 shows, this cumulative forecast surpasses the CEJP target.

6.2 California’s Wind Market

This section estimates the total technical, economic and market potentials for distributed wind energy in

California and in the three major IOU service territories. The potential estimates target wind turbines 50

kW to 5 MW that could be located on the customer side of the meter.

Overall Approach

We estimate wind technical potential using California wind resource data24 and wind turbine operating

characteristics (e.g., expected capacity factors at given wind speeds and turbine height) for wind turbines

in the size range of 50 kW to 5 MW.25 We restrict wind applications to single-site applications (i.e., instead

of grouping wind turbines into “farms,” a single-site application associates one or more wind turbines

directly to a customer site to help meet that customer’s electricity demands) and turbine hub heights of

no more than 80 meters (approximately 260 feet).26 In addition, we follow land use exclusions set forth

by NREL for wind resource modeling, which excludes wilderness areas, parks, urban areas, and bodies of

water.27 Distributed wind energy systems under SGIP are located behind the customer meter, so we use

Census information on numbers of select business types in each county as possible host sites for

distributed wind turbine installations.

24 Sources of California wind resource data include CEC PIER wind resource data; and NREL’s wind data site

(http://www.nrel.gov/gis/data_wind.html); and U.S. DOE EERE (http://energy.gov/eere/wind/wind-resource-assessment-and-characterization)

25 Wind turbine capacity is based on wind speed. The maximum power output (the nameplate capacity) coincides with the

maximum wind speed at which the wind turbine can safely operate. Power output from wind turbines vary with wind speed and wind turbine manufacturers provide power curves that provide estimated power output of the turbine based on wind speed at the hub height. Note that at very high wind speeds (generally above 50 to 55 mph, the rotor on the wind turbine is placed into a locked position and the turbine rotates without load (no production of power). For additional details on wind turbine performance, please see Erich Hau’s book “Wind Turbines: Fundamental Technologies, Application, Economics: 2nd edition, 2010 or Appendix A of 2015 Self-Generation Incentive Program Cost Effectiveness Study,” October 2015; final report pending release

26 We adopt a maximum “hub” height of 80 meters as this is the height needed to get into higher wind resources but still

economically viable for host customers.

27 NREL environmental exclusions include federal, state or private lands designated as park, wilderness, wilderness study area,

national monument, national battlefield, recreation area, national conservation area, wildlife refuge, wildlife area, wild and scenic-river or inventoried roadless area. Excluded lands also include those lands with slopes greater than 20%. See Department of Energy’s Wind Exchange site at http://apps2.eere.energy.gov/wind/windexchange/windmaps/resource_potential.asp

http://www.nrel.gov/gis/data_wind.html





We estimate wind economic potential using the total resource cost (TRC) results generated from the

SGIPce model28 for wind turbines at nominal capacities of 50 kW and 1.5 MW with no incentives.29 Our

criteria is that wind systems are economic for all instances where TRC results are equal to or exceed 0.8.

Wind Technical Potential Results

In developing wind technical potential, we first examine the amount of area in each county with sufficient

wind resource to generate power.

Wind resource is typically classified by wind speeds and estimated wind power densities. Table 6-6 lists

wind power classes from 1 through 7 based on associated wind speeds at different heights.30 In general,

wind speeds in class 3 and above are considered minimums for commercial wind energy systems.

28 See “2015 Self-Generation Incentive Program Cost Effectiveness Study,” October 2015; final report pending release

29 While we are investigating wind resource potential up through 5 MW in capacity, we use the nominal 50 kW and 1.5 MW

ratings as these were the capacities modeled in the SGIP cost effectiveness study and have cost effectiveness results. In addition, we currently see most distributed wind turbine installations coming in at no more than the 1.5 – 2 MW size range for “single-site” applications.

30 Wind power classes from NREL: http://www.nrel.gov/gis/wind_detail.html. Note that wind power classes are meant to

overlap two power density groups.

http://www.nrel.gov/gis/wind_detail.html



Table 6-6: Wind Power Classes and Speeds

Wind Power

Class

10 m (33 ft) 50 m (164 ft)

Wind Power

Density (W/m2)

Speed (b) m/s

(mph)

Wind Power

Density (W/m2)

Speed (b) m/s

(mph)

1 0 0 0

100 4.4 (9.8) 200 5.6 (12.5)

2

150 5.1 (11.5) 300 6.4 (14.3)

3

200 5.6 (12.5) 400 7.0 (15.7)

4

250 6.0 (13.4) 500 7.5 (16.8)

5

300 6.4 (14.3) 600 8.0 (17.9)

6

400 7.0 (15.7) 800 8.8 (19.7)

7 1000 9.4 (21.1) 2000 11.9 (26.6)

Table 6-7 lists the approximate amount of land area available by county (in square miles) and by average

annual wind speed categorized by wind class speed. We limit estimates of potential to areas of class 3 or

greater wind speeds.31

Table 6-7: Area by Wind Class Speed (square miles): 50 Meter Height

County

Area by Class Wind Speed (sq. miles)

Class 3 Class 4 Class 5 Class 6 Class 7 Total

Alameda 14.1 3.8 3.9 0.2 22.0

Alpine 43.6 21.5 12.8 10.8 5.8 94.4

Amador 1.8 0.6 0.3 0.2 2.9

Butte 12.1 2.0 0.4 14.4

Calaveras 1.8 0.0 1.9

Colusa 5.5 0.7 0.1 0.0 6.4

Contra Costa 12.8 2.6 1.0 0.0 16.3

Del Norte 48.4 15.8 5.1 1.8 0.1 71.2

El Dorado 16.3 7.9 5.0 5.4 3.7 38.4

Fresno 40.0 15.9 7.3 4.4 1.3 68.9

Glenn 2.2 0.4 0.3 0.0 2.9

Humboldt 90.9 39.3 22.9 16.3 9.0 178.4

Imperial 184.8 42.1 11.6 9.6 1.2 249.4

Inyo 671.9 241.1 115.0 75.6 28.8 1,132.3

Kern 268.4 242.8 167.2 172.9 67.7 919.0

31 Wind classes 1 and 2 are excluded because wind speeds in these classes generally do not have sufficient speed to meet the

cut-in speed requirements of turbines.



County

Area by Class Wind Speed (sq. miles)

Class 3 Class 4 Class 5 Class 6 Class 7 Total

Kings 0.7 0.1 0.9

Lake 13.8 1.9 0.2 16.0

Lassen 89.9 24.2 8.7 4.3 1.0 128.1

Los Angeles 285.3 241.6 144.2 64.7 15.6 751.4

Madera 10.8 5.0 2.8 2.6 1.1 22.3

Marin 16.6 7.6 4.0 0.6 28.8

Mariposa 1.4 0.6 0.2 0.2 0.0 2.4

Mendocino 40.9 6.8 1.0 0.1 48.9

Merced 31.4 6.7 2.7 0.5 41.3

Modoc 60.5 24.9 13.7 12.3 6.8 118.2

Mono 149.8 68.4 41.4 41.8 30.8 332.2

Monterey 30.0 10.0 2.2 0.8 0.2 43.2

Napa 3.5 0.4 0.2 0.1 4.2

Nevada 8.8 2.5 0.9 0.4 0.1 12.7

Orange 33.2 9.7 2.2 0.2 45.3

Placer 13.7 5.4 2.3 1.9 0.6 24.0

Plumas 48.5 13.9 4.4 2.5 0.4 69.7

Riverside 645.2 292.1 125.0 84.5 45.4 1,192.2

Sacramento 0.1 0.1

San Benito 4.3 0.5 0.0 4.9

San Bernardino 2,008.2 645.1 186.7 101.1 25.9 2,967.0

San Diego 238.5 141.0 71.1 48.7 24.8 524.0

San Francisco 0.4 0.4

San Joaquin 1.6 0.8 0.0 2.5

San Luis Obispo 30.6 5.2 1.5 0.1 37.3

San Mateo 14.4 2.5 0.8 0.1 17.7

Santa Barbara 169.1 115.3 57.5 39.5 15.7 397.1

Santa Clara 0.5 0.5

Santa Cruz 0.5 0.5

Shasta 99.0 33.5 15.2 11.5 5.0 164.1

Sierra 32.6 11.0 4.3 2.7 0.6 51.2

Siskiyou 179.7 64.8 25.7 17.8 10.7 298.6

Solano 37.1 53.0 1.8 0.6 92.4

Sonoma 14.4 2.9 0.7 0.1 18.1

Stanislaus 2.9 0.1 3.0

Sutter 3.4 0.6 0.1 0.0 4.1

Tehama 36.4 4.9 1.0 0.2 42.5

Trinity 36.0 11.8 5.2 2.4 0.2 55.6

Tulare 43.9 19.3 9.4 8.4 2.3 83.2

Tuolumne 29.1 9.8 5.3 4.9 1.4 50.5

Ventura 139.6 64.8 29.5 14.4 1.8 250.1

Yolo 9.0 1.7 0.2 10.9

State Totals 11,837.9 6,618.5 3,489.7 4,701.3 882.5 27,530.0



Figure 6-13 is a map showing the distribution of wind speeds with hub heights of 50 meters (approximately

164 feet) by county.32 At hub heights of 50 meters, most of the state consists of low wind resources. For

the most part, those regions of California with class 4 wind speeds and higher were developed in the

1980’s as wind parks.33 Economic considerations limited development of the lower class wind areas until

recently. Improvements in wind turbine technology have made it possible for wind energy developers to

build projects in the lower wind speed resource areas. Wind energy projects developed under the SGIP in

recent years indicate the increased interest shown by wind developers in these lower wind-speed regions.

As indicated earlier, we are interested in the wind energy potential associated with wind projects in the

50 kW to 5 MW size range. For these types of wind projects, we also look at wind energy potential where

the gross annual capacity factor is 35% or greater.34

32 While we place a maximum hub height of 80 meters as the ceiling, our wind resource assessment is limited to 50 meter hub

height due to limited availability of granular wind resource information at 80 meters.

33 In particular, high wind resource areas located at San Gorgonio, Altamont Pass and Tehachapi were the focus of large wind

parks. Total installed capacities in these areas are 615 MW at San Gorgonio, Altamont at over 570 MW and Tehachapi at over 700 MW. Additional large scale wind farms have been developed in Kern County (Alta Wind Farm) and Solano County (Montezuma Hills) but post 1980’s.

34 Gross annual capacity factor represents the ratio of its actual output over the year to its potential annual output if it were

possible for it to operate at full nameplate capacity. Our use of 35% gross capacity is conservative as wind turbine technologies being developed currently can be expected to have annual capacities of 40% or more, depending on the wind resource. Because we are using average annual wind resource estimates, we are able to associate the gross annual capacity factor to the average annual wind speeds.



Figure 6-9: Distribution of Wind Speeds by County (50 Meters)

Table 6-8 provides estimates of the potential capacity for wind turbines in the 50 kW to 5 MW size range

with associated annual gross capacity factor (GCF) of greater than 35%.

Table 6-8: Potential Wind Capacity with GCF > 35% by County (MW)

County

Potential Capacity

County

Potential Capacity

(MW) (MW)

Alameda 33 Orange 68

Alpine 142 Placer 36

Amador 4 Plumas 105

Butte 22 Riverside 1,788

Calaveras 3 Sacramento 0

Colusa 10 San Benito 7

Contra Costa 24 San Bernardino 4,451

Del Norte 107 San Diego 786

El Dorado 58 San Francisco 1

Fresno 103 San Joaquin 4

Glenn 4 San Luis Obispo 56

Humboldt 268 San Mateo 27



County

Potential Capacity

County

Potential Capacity

(MW) (MW)

Imperial 374 Santa Barbara 596

Inyo 1,698 Santa Clara 1

Kern 1,378 Santa Cruz 1

Kings 1 Shasta 246

Lake 24 Sierra 77

Lassen 192 Siskiyou 448

Los Angeles 1,127 Solano 139

Madera 33 Sonoma 27

Marin 43 Stanislaus 5

Mariposa 4 Sutter 6

Mendocino 73 Tehama 64

Merced 62 Trinity 83

Modoc 177 Tulare 125

Mono 498 Tuolumne 76

Monterey 65 Ventura 375

Napa 6 Yolo 16

Nevada 19 Grand Total 16,166

Figure 6-10 shows a ranking of the counties that represent 90% of the technical wind energy potential by

capacity (MW). According to the information in Figure 6-10, counties in Southern California appear to be

better prospects for low wind speed project development (from a technical potential perspective).

Figure 6-10: Counties Representing 90% of Wind Technical Potential

The gross potential of Table 6-8 assumes installation of wind turbines in this size range regardless of

availability of host customer site. However, we assume that distributed wind turbines are located at host

customer sites to help displace onsite electricity demand. There are still only a limited number of

distributed wind turbine installations in California. It is difficult to determine business types that would be



the best candidates for hosting wind turbines. We selected two general categories of possible candidate

business types based on the limited amount of historical data on distributed wind in California:

manufacturing/industrial facilities and retail trade businesses.35

Table 6-9 provides a list of “indicator” businesses that represent possible host sites for distributed wind

turbine installations.

Table 6-9: Counts of Indicator Businesses for Distributed Wind Systems by Type by County

County

2013

County

2013

Manufacturing Retail Trade Manufacturing Retail Trade

Alameda 1,800 4,245 Orange 4,637 9,461

Alpine 0 2 Placer 249 1,236

Amador 38 121 Plumas 15 84

Butte 176 736 Riverside 1,477 5,044

Calaveras 37 135 Sacramento 742 3,503

Colusa 24 59 San Benito 65 106

Contra Costa 535 2,502 San Bernardino 1,768 4,750

Del Norte 8 58 San Diego 2,861 9,297

El Dorado 157 522 San Francisco 684 3,595

Fresno 575 2,481 San Joaquin 511 1,595

Glenn 25 75 San Luis Obispo 382 1,158

Humboldt 128 573 San Mateo 611 2,041

Imperial 52 468 Santa Barbara 451 1,505

Inyo 10 87 Santa Clara 2,319 4,925

Kern 396 1,879 Santa Cruz 298 877

Kings 59 280 Shasta 134 641

Lake 32 171 Sierra 2 7

Lassen 1 80 Siskiyou 32 170

Los Angeles 12,478 28,442 Solano 251 1,069

Madera 96 317 Sonoma 828 1,795

Marin 226 1,033 Stanislaus 395 1,399

Mariposa 15 65 Sutter 57 284

Mendocino 118 448 Tehama 37 147

Merced 106 537 Trinity 10 53

Modoc 1 30 Tulare 236 1,046

Mono 7 71 Tuolumne 62 180

Monterey 270 1,313 Ventura 869 2,592

Napa 448 531 Yolo 169 466

Nevada 145 398 Yuba 39 131

We can use the count of indicator businesses to develop an estimate of the countywide and statewide

capacity if wind turbines in the nominal 50 kW or 1.5 MW size range could be installed in each of the

businesses. Table 6-10 lists the potential wind capacity associated with deployment of nominal 50 kW or

35 To date, distributed wind energy systems in California have generally been located at manufacturing/industrial sites (e.g.,

Anheuser-Busch, Cemex, etc.), retail trade spaces (e.g., Walmart) or large agricultural operations (e.g., Superior Farms).



1.5 MW wind turbines associated with each business. Overall, locating one wind turbine of 50 kW or 1.5

MW capacity at the representative businesses corresponds to a stateside technical potential of

approximately 1.4 GW and 9.8 GW, respectively.

Table 6-10: Number of Potential Installations of 50 kW and 1.5 MW Wind Turbines

County

Potential Capacity (MW)

County


At 50 kW At 1.5 MW At 50 kW At 1.5 MW

Alameda 33 33 Orange 68 68

Alpine 0 0 Placer 12 36


Butte 9 22 Riverside 74 1,788

Calaveras 2 3 Sacramento 0 0


Contra Costa 24 24 San Bernardino 88 2,652

Del Norte 0 12 San Diego 143 786

El Dorado 8 58 San Francisco 1 1

Fresno 29 103 San Joaquin 4 4

Glenn 1 4 San Luis Obispo 19 56

Humboldt 6 192 San Mateo 27 27

Imperial 3 78 Santa Barbara 23 596

Inyo 1 15 Santa Clara 1 1

Kern 20 594 Santa Cruz 1 1




Los Angeles 624 1,127 Solano 13 139

Madera 5 33 Sonoma 27 27

Marin 11 43 Stanislaus 5 5




Modoc 0 2 Tulare 12 125


Monterey 14 65 Ventura 43 375

Napa 6 6 Yolo 8 16

Nevada 7 19 Grand Total 1,410 9,759

Technical Potential by IOU Service Territory

Table 6-11 is a summary list of the technical wind energy potential by IOU service territory. At nearly

12,000 MW, SCE represents nearly 3/4ths of the state’s technical distributed wind energy potential.



Table 6-11: Technical Wind Potential by IOU: (50 Meters)


Statewide PG&E SCE SDG&E

16,166 3,401 11,979 786

100.0% 21.0% 74.1% 4.9%

The reason why SCE dominates the wind energy technical potential is more clearly seen in Figure 6-11.

This figure shows a map of the wind resource areas at 50 meters with an overlay of the IOU service

territories. It is clear that much of the higher wind resource in California at 50 meters is located in the SCE

service territory.

Figure 6-11: Distribution of Wind Class Speeds in Counties with IOU Overlay (50 Meters)



Wind Economic Potential Results

We use results from the SGIPce cost effectiveness model to assess wind energy economic potential.

Consistent with our treatment of all the DG and energy storage technologies, we assume wind energy

technologies with a TRC benefit-to-cost ratio of 0.80 or better are cost effective. Under that condition,

the technical potential becomes the economic potential. Figure 6-12 is a bubble chart showing the TRC

ratios of the nominal 50 kW and 1.5 MW wind turbines modeled using the SGIPce model.

Figure 6-12: Wind Economic Potential by Nominal Capacity and Year

Based on the TRC ratios, only the larger wind turbine systems in the nominal 1.5 MW capacity have

economic potential prior to 2024. We also assume that the 1.5 MW wind turbines are associated with the

representative businesses that were shown earlier in Table 6-9. Table 6-12 shows the economic potential

for distributed wind energy in California by county. For the larger wind turbine systems with nominal

capacity of 1.5 MW, the economic potential is the same as the estimated 9.8 GW technical potential

(assuming one 1.5 MW turbine located at a host site). Per our earlier assumptions, it would require the

installation of over 6,500 wind turbines rated at 1.5 MW of capacity to achieve the distributed wind energy

potential.



Table 6-12: Economic Potential for Distributed Wind Energy

County

Economic

Capacity (MW)

No of Installs

at 1.5 MW County

Economic

Capacity (MW)

No. of Installs

at 1.5 MW

Alameda 33 22 Orange 68 45

Alpine 0 0 Placer 36 24


Butte 22 14 Riverside 1,788 1,192

Calaveras 3 2 Sacramento 0 0


Contra Costa 24 16 San Bernardino 2,652 1,768

Del Norte 12 8 San Diego 786 524

El Dorado 58 38 San Francisco 1 0

Fresno 103 69 San Joaquin 4 3

Glenn 4 3 San Luis Obispo 56 37

Humboldt 192 128 San Mateo 27 18

Imperial 78 52 Santa Barbara 596 397

Inyo 15 10 Santa Clara 1 1

Kern 594 396 Santa Cruz 1 0




Los Angeles 1,127 751 Solano 139 92

Madera 33 22 Sonoma 27 18

Marin 43 29 Stanislaus 5 3




Modoc 2 1 Tulare 125 83


Monterey 65 43 Ventura 375 250

Napa 6 4 Yolo 16 11

Nevada 19 13 Grand Total 9,759 6,506

Since inception of the SGIP in 2001, nearly 25 MW of distributed wind energy capacity has been installed

under the program. Figure 6-13 shows the growth of wind capacity under the SGIP by year. However, this

represents only a small fraction of the state’s economic wind potential.



Figure 6-13: Amount of Wind Energy Installed under the SGIP as of 2014

Wind Market Potential

In assessing the market potential for wind, we examine different market growth rates using market

saturation curves. In particular, using historical wind turbine capacity data and logistic modeling we

assumed 10%, 20% and 30% average annual growth rates to calculate future market potential levels. This

allowed us to estimate the future market potential as a percentage of the economic potential and assess

the reasonableness of both the growth rate and percentage penetration of the economic potential. Figure

6-14 depicts how cumulative installed wind capacity could look in California assuming an average annual

growth rate of 10% using a logistic modeling approach. Wind capacity would grow from approximately 25

MW of capacity in 2015 to approximately 250 MW by 2038. The maximum annual growth rate of

approximately 14 MW would occur shortly after 2018.




Figure 6-15 shows cumulative wind capacity in California under an average annual growth rate of 20%.

Under this scenario, cumulative wind capacity reaches approximately 1.2 GW by 2038. This represents

over 12% of the statewide economic potential estimated earlier assuming installation of 1.5 MW wind

turbines at representative businesses.




Figure 6-16 represents cumulative wind capacity in California assuming an average annual growth rate of

30%. In this situation, California realizes a cumulative installed wind capacity of approximately 2.5 GW by

2038. This represents nearly 26% of the statewide economic potential.

While a 30% average annual growth rate may be a high target for distributed wind, 20% may be

achievable. However, this magnitude of growth rate for wind would require significant changes in policies

and support.




6.3 California’s Biogas Power Generation Market

In this section, we estimate technical, economic and market potential for California’s distributed

generation biogas market.36 Sources of biogas include anaerobic digesters used in dairy operations,

landfills, wastewater treatment plants and food processing facilities. We focus our estimates on landfills,

wastewater treatment plants and dairy operations. These are among the main sources of biogas for

distributed generation applications. We examine the potential by county and IOU service territory. We

further discuss potential based on the nominal generator sizes and show how many potential projects

could be installed if the specific generation technology was used and the potential distribution of projects

based on the nominal sizes. The nominal sizes used in the SGIP cost effectiveness model (SGIPce) for

internal combustion engines are 1,500 kW and 500 kW; for fuel cells 500 kW and 1,200 kW; for gas

turbines 2,500 kW; and, for microturbines 200 kW. In identifying economic potential, we assume any

technology with a TRC benefit-to-cost ratio of greater than 80% is economic.

36 We refer to distributed generation applications to distinguish them from larger-scale (e.g., 20 MW and larger) direct

combustion biomass projects that were installed in California in the 1980’s and 1990’s.



Overall Approach

Landfill data used is obtained from EPA’s Landfill Methane Outreach Program (LMOP) database.37 This

data includes landfill gas to energy sites that are operational or under construction. These sites have

generators planned or already in operation. The generation potential for sites not explicitly specified in

the database are estimated based on the daily gas production information, which is provided in the

database. In addition to these sites, there are other sites that are identified as candidate sites for future

biogas development. These aggregations of operational sites and candidate sites are used to determine

the overall technical potential.

Dairy biogas estimates are obtained using historical dairy animal population data available from the

California Department of Food and Agriculture (CDFA).38 These data show the number of milking cows in

California each year by county. We use this population data to estimate the technical potential based on

an estimate of 30 cubic feet of methane produced per day per animal.39 We further assume that the

generators operate 20 hours per day. Economic potential is estimated by filtering the same data to

remove operations that have less than 250 animals. This is the minimum number of dairy cows needed to

generate enough biogas to operate a 25 kW internal combustion engine. The CDFA data specifically tracks

dairy milking cows. This approach produces a conservative estimate of technical and economic potential

because it ignores dry lot and replacement animals present in most operations. If these animals were also

included, we could increase the potential biogas production by 30-50%.

Wastewater treatment plants and food processing facilities biogas estimates are obtained using data from

the California State Water Resources Control Board.40 These data show daily flow, annual flow and biosolid

loads, which are used to estimate potential biogas production. We estimate generation capacity based on

a 30% conversion efficiency and further assume the generators operate 20 hours per day. California has

over 240 wastewater treatment plants and some of them actively recover biogas to generate electricity.

The economic potential is based on the TRC results from the SGIPce model for biogas operations.

Consistent with that model and study, we assume that a technology is a cost effective resource if it has a

TRC benefit-to-cost ratio of 0.8 or greater. In the case of dairy operations, the collection costs include the

digester cover needed to capture the gas. For landfill and wastewater applications, we do not include

cover costs because these are required by statute at landfills. The nominal cases tested are 500 kW and

1,500 kW for IC engines, 2,500 kW for gas turbines, 500 kW and 1,200 kW for fuel cells, and the 200 kW

for microturbines. All nominal cases passed the TRC criteria of exceeding 0.8 and were considered cost

effective.

37 EPA’s database is found at: http://www.epa.gov/lmop/

38 California Department of Food and Agriculture Statistics, from http://www.cdfa.ca.gov/statistics/

39 Donald L. Van Dyne and J. Alan Weber, Biogas Production from Animal Manures: What Is the Potential? Industrial Uses/TUS-

4/December 1994

40 http://www.swrcb.ca.gov/



Landfill Gas Results

To determine the landfill biogas economic potential capacity we use the EPA LMOP database and segment

the available resources by county. The economic potential shows systems that are already operating or in

the planning stage. The technical potential is determined using the same database with the addition of

candidate sites. Candidate sites are expected to produce landfill biogas in the near future (i.e., within five

years). Table 6-13 shows the potential resource generation capacity ranked by quantity and county as well

as the statewide totals. Total technical and economic potential are 457 MW and 404 MW respectively.

Los Angeles County has the highest capacity by far with an expected economic capacity of over 185 MW.

The statewide landfill gas technical potential is nearly 460 MW and the statewide economic potential is

about 404 MW.

Table 6-13: Landfill Gas Technical and Economic Potential by County

County


Capacity (MW)


Capacity (MW)

Los Angeles 185 190

Orange 32 32

San Diego 26 28

Alameda 21 27

Sacramento 18 18

Ventura 18 18

Santa Clara 16 23

San Mateo 13 13

Contra Costa 11 11

Sonoma 10 10

Others 53 88

Grand Total 404 457

Figure 6-17 shows the span of landfill gas technical potential among the top fifteen counties, representing

90% of the statewide technical potential. Again, Los Angeles has far greater technical potential; primarily

due to the number of large landfill operations in the county.



Figure 6-17: Ranking of Landfill Gas Technical Potential by County

Table 6-14 breaks down the resource available within each IOU service area and illustrates the distribution

of nominal 500 kW IC engine systems. The number of installs are determined by assessing the potential

resource at each site and determining how many multiple installations of a 500 kW IC engine could use

the available biogas. Empirical evidence shows that most operators install multiple modular generators.

This distribution shows that 50% of available sites would need one to four-500 kW units to make full use

of the available landfill gas resource.



Table 6-14: Distribution of Nominal 500 kW IC Engines by IOU

Resource

Availability

Bin based on

500 kW

Increments

Pacific Gas &

Electric Co

San Diego Gas &

Electric Co

Southern California

Edison Co Total

Possible

No. of

500kW

Installs

Total

Possible

Capacity

(kW)

Possible

No. of

500kW

Installs

Total

Possible

Capacity

(kW)

Possible

No. of

500kW

Installs

Total

Possible

Capacity

(kW)

Possible

No. of

500kW

Installs

Total

Possible

Capacity

(kW)

0-500 13 1,376 3 490 16 1,866

500-1000 11 9,092 3 1,650 14 10,742

1000-1500 14 17,600 2 2,754 4 4,742 20 25,096

1500-2000 11 18,026 1 1,700 12 19,726

2000-2500 5 10,295 5 11,206 10 21,501

2500-3000 5 13,821 4 10,701 9 24,522

3000-3500 8 26,083 4 13,000 1 3,000 13 42,083

3500-4000 2 7,400 1 3,700 3 11,100

4000-4500 1 4,300 3 12,350 4 16,650

4500-5000 1 4,649 2 9,900 3 14,549

>5000 8 58,752 4 38,025 16 172,850 28 269,627

Grand Total 79 171,394 12 59,179 41 226,890 132 457,463

The economic potential for landfill gas technologies is determined using the 80% TRC cost to benefit ratio

minimum derived from the SGIPce model. Figure 6-18 summarizes the TRC test results for the different

prime movers that could be used for landfill biogas to energy operations by nominal capacities used in the

SGIPce model. In general, all the prime movers achieve a TRC ratio of 80% or greater starting in 2014. The

TRC ratios also increase moving into the future. Figure 6-18 shows that the nominal 5 MW gas turbine

technology has the highest TRC ratios for landfill biogas applications.



Figure 6-18: TRC Benefit-to-Cost Ratios for Selected Landfill Biogas Generation Technologies

Dairy Biogas Results

We use milking animal population data from the California Department of Food and Agriculture41 to

estimate the potential biogas production from dairy animals. For the technical potential criteria, we use

all dairy operations. For the economic potential, we limit our analysis to dairies that have more than 250

milking cows. A minimum of 250 milking dairy cows is assumed to be the number of animals needed to

generate enough biogas to operate a 25 kW IC engine unit operating 20 hours per day. Table 6-15 shows

a summary of the CDFA Dairy Statistics data by county for 2014 ranked by technical potential. The total

technical and economic potential are 137 MW and 135 MW respectively. Tulare, Merced, and Kings

County account for over 50% of the technical potential.

41 http://www.cdfa.ca.gov/dairy/pdf/Annual/ California Dairy Statistics 2014_Data.pdf



Table 6-15: Dairy Biogas Economic and Technical Potential by County

County Total Number of Dairies


Capacity (kW)


Capacity (kW)

Tulare 281 37,251 37,251

Merced 228 21,258 21,258

Kings 119 14,137 14,137

Stanislaus 207 13,837 13,837

Kern 51 12,873 12,873

Fresno 79 8,995 8,995

San Joaquin 113 7,918 7,918

Madera 43 6,033 6,033

Imperial 62 3,960 3,960

Riverside 28 3,042 3,042

Sonoma 65 2,148 2,148

Glenn 31 1,287 1,287


Humboldt 60 1,037

Marin 25 585 585

San Bernardino 3 398 398

Tehama 11 269 269

Del Norte 7 217 217

Yuba 4 215 215

San Diego 3 148 148

Siskiyou 3 51

Grand Total 1,453 135,654 136,742

Table 6-15 shows the counties that represent the 90% of California’s dairy technical potential. All of the

counties with potential are in the PG&E service territory except for San Bernardino (SCE).



The economic potential is determined using the 250 milking dairy cows’ minimum criteria and the 80%

TRC minimum derived from the SGIPce model. The TRC gives the cost benefit ratio for each generation

technology. The costs for the dairy biogas case includes the cost of the digester cover and this is not

included the landfill biogas case. Furthermore, because we only consider onsite biogas generation, no

resource transportation costs are included in the TRC. Onsite dairy biogas generation is limited by the

number of animals and the scale of dairy biogas operations, and thus, is much smaller than for a landfill

or wastewater operation. Figure 6-20 shows that the nominal 200 kW microturbine technology has the

highest TRC for dairy biogas applications and this matches what we would expect given that the average

milking dairy herd in California42 is 1,100 animals. In general, the size and capital cost of a 200 kW

microturbine system matches the scale of most dairy operations in California. Fuel cells have the lowest

TRC cost benefit ratios because of the high capital costs associated with the fuel cell as well as the required

gas clean-up system.

42 http://www.cdfa.ca.gov/dairy/pdf/Annual/ California Dairy Statistics 2014_Data.pdf

Figure 6-19: Counties Representing 90% of Dairy Biogas Technical Potential



Figure 6-20: TRC Benefit-to-Cost Ratios for Selected Dairy Biogas Generation Technologies

Wastewater Treatment Plants Biogas Results

We used data from the California State Water Resource Board to estimate the biogas production technical

potential for wastewater treatment plants. The electric generation capacity is estimated from the

estimated biogas at a conversion efficiency of 30% and a generator capacity of 83%. We estimate the

economic potential by filtering out all sites that have an estimated generation capacity of less than 100

kW. Table 6-16 shows the potential by county ranked by capacity. Total economic and technical potential

are estimated as 125 MW and 130 MW, respectively. Los Angeles County has the highest capacity at 33

MW for both economic and technical potential.



Table 6-16: Wastewater Treatment Plants Biogas Economic and Technical Potential by County

County

Number of

Sites

Total Economic

Potential (kW)

Total Technical

Potential (kW)

Los Angeles 8 33,258 33,305

Orange 6 11,420 11,510

San Diego 6 9,895 10,173

Santa Clara 4 9,533 9,533


Alameda 7 6,732 6,732

Riverside 13 6,432 6,705

San Bernardino 10 4,354 4,476

Fresno 4 3,252 3,341

San Francisco 2 2,927 3,003

Contra Costa 4 2,704 2,965

Kern 7 2,263 2,552

Ventura 8 2,459 2,459

San Joaquin 4 2,352 2,352

Stanislaus 2 2,057 2,235

San Mateo 7 2,107 2,186

Solano 4 1,714 1,786

Santa Cruz 2 1,328 1,328

Others 45 12,196 15,194

Grand Total 146 125,264 130,127

Figure 6-21 shows the span of wastewater treatment plants biogas technical potential among the top

counties, which represent about 90% of the statewide technical potential. Los Angeles County accounts

for about 26% of the total technical biogas potential.



Figure 6-21: Ranking of Wastewater Treatment Plants Biogas Potential by County

Summary of California’s Biogas Potentials

Figure 6-22 summarizes the technical, economic and market potential of distributed biogas generation

capacity in California. For 2014, we estimate a technical potential of about 724 MW, an economic

potential of about 665 MW and a market potential of 234 MW. The market potential is that fractional

portion of the economic potential that meets the SGIPce TRC criteria. It also meets a saturation target

modeled using a logistic curve as shown below in Figure 6-23. We assume a market penetration of 40%

for landfill and wastewater treatment plant biogas and 20% for dairy biogas43 of the economic potential.

With these limits, we estimate a market potential of 268 MW. The logistic curve indicates we can expect

to meet the SB 1122 target of 200 MW of biogas generation in California by 2024.44

Existing SGIP biogas projects represent approximately 36 MW or about 5% of the state’s economic biogas

potential. Figure 6-23 shows the actual installations in SGIP and the projected market growth based on a

268 MW market potential. The actual SGIP biogas installations are used as the template for modeling the

market growth and meeting the SB 1122 target. The logistic curve model indicates that the growth in

43 California Public Utilities Commission, “Final Consultant Report-Small Scale Bioenergy: Resource Potential, Costs and Feed-

in Tariff Implementation Assessment”, October 31, 2013

44 SB 1122 has an overall bioenergy target of 250 MW. However, 50 MW is to be derived from “byproducts of sustainable forest

management.” As we are focusing solely on biogas derived energy from landfills, wastewater treatment systems and dairies, we exclude that 50 MW from the target.



biogas generation exceeds what would be expected in the beginning, which is presumably the effect of

the biogas adder in the earlier years of the program. The relative proportion of the different SGIP

technologies is shown in Figure 6-22. SGIP generation technology consists of 50% internal combustion

engines, 34% fuel cells, and 16% microturbines and consists predominantly of generation units less than

5 MW. Landfill biogas sources have the quantity of biogas needed to run gas turbines and larger

generation capacities are not represented in the SGIP.

Figure 6-22: Total Technical, Economic, and Market Potential Growth 2014-2024 (kW) with SGIP Biogas Projects

Figure 6-23 shows the market growth based on the market potential of 268 MW. The curve shows biogas

generation meeting the SB 1122 target in 2030. In addition, the figure shows the annual growth rate

needed to attain the market potential. The peak growth rate is 22 MW in 2019. The average annual growth

rate in capacity is 17%, whereas the cumulative generation growth rate is about 34%.



Figure 6-23: Biogas Generation Market Growth Forecast

Table 6-17 shows resource potential by IOU. PG&E has the highest biogas generation potential.

Table 6-17: Technical, Economic, and Market Biogas Generation Potential by IOU

IOU

Total Technical

Potential Capacity (kW)

Total Economic

Potential Capacity (kW)

Total Market Potential

Capacity (kW)

Pacific Gas & Electric 360,221 314,288 110,059

San Diego Gas & Electric 81,009 79,735 36,355

Southern California Edison 283,101 271,886 122,165

Grand Total 724,332 665,910 268,579

6.4 California’s Advanced Energy Storage Market

In this section, we estimate the total technical, economic, and market potential for customer-sited

advanced energy storage (AES) capacity at buildings in the service territories of California’s electric IOUs.

We consider market segments in the residential, commercial, institutional, and industrial sectors but

restrict the analysis to AES systems sized between 1 kW and 5 MW.



Overall Approach

Technical potential for customer-sited AES exists primarily for customer energy management,45 either

through reduced peak demand charges or energy arbitrage (shifting energy consumption to lower bill

periods).46 We estimate the technical potential for AES to be the capacity sufficient to reduce every hour’s

load to the average load of the greatest usage day of the month. In the future, the use of AES to firm

renewables and provide grid support may become viable value streams, but those do not currently exist

in California.

We use a bottom-up approach to estimate the AES technical potential for buildings served by California’s

electric IOUs. We begin with counts of all business establishments and residential units in the state in a

base year. We then group them by common market segment, size, and location. Next, for each group, we

model a full year of hourly electric loads.47 Then we model AES to flatten the top four hourly loads on any

given day.

We define a building’s hourly electrical demands as the sum of its hourly electrical end use demands.

Demand during on-peak hours is the primary target for discharge from the AES. We use Itron’s SitePro

software model to estimate electric demand. SitePro estimates hourly electricity consumption year-round

by end use for a large number of market segments, building sizes, and climates. Electricity end use loads

are then summed for individual hours. The AES discharges to reduce demand during the four hours with

the highest demand per day and charges only during off-peak hours. This demand reduction approach to

technical potential fundamentally serves to enhance AES economics. It does so by moving demand from

generally more expensive on-peak to lower cost off-peak hours but without regard to any system costs.

C or Power to Energy Ratio

AES is unique from other SGIP technologies in that it is rated on both power (MW) and energy (MWh)

capacities. System costs are a function of both power and energy but are more heavily driven by energy.

Battery sizing requires matching both the power and energy requirements needed to meet a site’s needs.

The ratio of power to energy is termed ‘C’ for batteries. For example, a battery that requires 4 hours to

discharge has a C of 1:4. A battery that could fully discharge in a single hour has a C of 1. A battery that

could discharge in 15 minutes has a C of 4. The SGIP sets C at 0.5 (1:2) setting the rated power capacity of

the battery at one-half the rated energy capacity of the battery. Many commercially available lithium ion

systems can far exceed the SGIP C of 0.5, and some can approach a C of 4, fully discharging in 15 minutes.

45 AES could also provide grid support and intermittent renewable firming. However, current California rules do not provide

any incentive for behind the meter AES to provide these services.

46 AES also has the potential to improve reliability by providing backup power for critical circuits during grid outages. This is

not a use that is supported by the SGIP so this study does not investigate the potential of backup and increased grid reliability.

47 We use Itron’s SitePro software to model hourly loads by end-use based on market segment, building size, and climate

region.



For example, a 40 kWh CODA Core UDP Tower would have a SGIP rating of 20 kW. However, it is capable

of 40 kW continuous discharge (C=1) and can produce up to 160 kW (C=4) for short periods.48

To match C ratios seen in the field, we calculated the technical potential by sizing the AES with enough

energy capacity to meet the difference between the highest and the fifth highest hourly energy

consumption during each site’s highest consumption day. This resulted in nearly a 1:4 power to energy

ratio.49 This means the AES would be essentially sized to flatten or ‘shave’ the top four peak hours on the

highest usage day at each site.

We then added a 20% buffer to provide a margin that allows the controller to preserve battery life better

by not fully discharging the battery on that peak day. Again, we did this to be consistent with the Cost

Effectiveness Model and current industry practice. To calculate an equivalent SGIP power capacity, we

divided this energy capacity by 2 (i.e., a power to energy ratio of 1:2).

Estimated AES Technical Potential

As done for CHP, we estimate AES technical potential for over 500 combinations of market segment, size,

and location. Total technical potential is the sum of the products of all these technical potentials and their

associated building counts. Below we list technical potentials for various combinations of market segment

as well as by electric IOU.

Potential by Market Segment and IOU Service Territory

Table 6-1Table 6-18 presents the technical potential based on the sums of energy storage needed by

segment. This technical potential is nearly 15 GW, as shown in Table 6-18. The majority of the technical

potential (approximately 53%) is in the single family residential sector. The total statewide potential is

18,854 MW.

48 https://www.sustainablesv.org/ecocloud//uploads/solutions/40_kWh_Single_DC_Tower_2014-1-31_Public.pdf

49 We chose 1:4 to match summer peak where the majority of the highest loads occur for any site.



Table 6-18: AES Technical Potential, Based on Energy Capacity to Shave Top 4 Hours50

Sector PG&E (MW) SCE (MW) SD&G (MW) Total (MW) Percent

College 23 27 6 55 0.37%


Food Store 125 144 27 296 1.98%

Health, Hospital 124 134 27 285 1.91%

Large Multifamily 174 225 44 444 2.97%

Lodging, Hotel 84 84 31 198 1.33%

Office, Large 805 1,263 254 2,322 15.54%

Office, Small 213 269 55 538 3.60%


Retail, Large 219 426 66 711 4.76%

School 29 38 7 73 0.49%

Single Family 3,538 3,671 636 7,846 52.51%

Small Multifamily 585 623 140 1,347 9.02%

Warehouse 135 197 24 357 2.39%

Grand Total 6,239 7,333 1,368 14,940

Percent 41.76% 49.08% 9.16%

Comparison of Potential Estimates to Other Studies

A number of studies have examined the potential of energy storage but few have focused on California.

A Sandia National Labs study51 assumed that for demand charge management, one-third of the total

California peak load “is in play” for demand charge management, which means that is the potential

amount of energy storage that could be installed to flatten the load and additional energy storage would

not be utilized. That would result in slightly higher potential (16,589) than shown in Table 6-18.52 However,

doing so would require significantly longer discharge periods (an average of 7 hours or C~0.14) than

systems are being designed for.53

Estimated AES Economic Potential

The economic potential of AES is the portion of the technical potential that meets a cost effectiveness

criterion, in this case a TRC test benefit-to-cost ratio of 0.8 or higher. The cost effectiveness criterion is

the same for each AES system regardless of size. System costs across sizes, on the other hand, differ

somewhat. Benefits from AES also differ between inland and coastal climate regions as well as between

IOUs. As system costs decline through time, more of the technical potential meets the criterion, assuming

no other changes in costs or benefits.

50 MW rating based on SGIP Requirements of 2 hours of discharge (i.e., C=0.5 or MW=MWH/2)

51 Sandia Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide sand2010sand

2010sand2010-0815

52 Based on the maximum load CAISO ever recorded, 50,270 MW in 2006:

(https://www.caiso.com/Documents/CaliforniaISOPeakLoadHistory.pdf)

53 If we size AES to reduce all loads to the monthly average per site, our technical potential is 132,492 MWh and 18,854 MW.



We used the SGIPce cost effectiveness model to identify systems with technical potential that met the

cost effectiveness criterion and so contribute to economic potential.

Figure 6-24 shows the TRC test results for residential energy storage from 2014 through 2024. Based on

the results, none of the locations is expected to become cost effective by 2024, so the economic potential

for this sector is zero. However, the SGIPce cost effectiveness model does not include AES projects paired

with other technologies, such as PV, that can increase benefits to society. Additionally, the Cost

Effectiveness Model does not value system backup that could change these results, as could the potential

development of residential demand charges and the potential to monetize grid support services.

Figure 6-24: TRC Benefit-to-Cost Ratios for Residential AES

Figure 6-25 shows the TRC test results for nonresidential energy storage. Some of the 30 kW systems at

some locations show a TRC result greater than 0.8 starting as early as 2018. However, both system sizes

and all locations have TRC ratios greater than 0.8 by 2024, meaning that all of these options should be

cost effective by that time. If future costs fall faster than predicted, this cost effectiveness point may come

earlier. Conversely, if future costs do not fall as quickly as expected, the cost effectiveness point may be

delayed.



Figure 6-25: TRC Benefit-to-Cost Ratios for Nonresidential AES

We can then filter the results of Table 6-18 to leave only the locations and system sizes that are expected

to achieve a TRC of 0.8 by 2024, based on the data of Figure 6-24 and Figure 6-25. This yields Table 6-19,

which shows our estimated economic potential for AES in 2024. Essentially, all residential potential is

removed but all nonresidential potential remains. This assumes that government and nonprofit entities

take advantage of energy services agreements that allow a third party to take advantage of tax and other

benefits to help the host reduce costs.



Table 6-19: Economic Potential for AES by Market Segment and IOU by 2024

Sector PG&E (MW) SCE (MW) SDG&E (MW) Total (MW) Percent

College 23 27 6 55 1.04%


Food Store 125 144 27 296 5.59%

Health, Hospital 124 134 27 285 5.38%

Lodging, Hotel 84 84 31 198 3.74%

Office, Large 805 1,263 254 2,322 43.79%

Office, Small 213 269 55 538 10.14%


Retail, Large 219 426 66 711 13.41%

School 29 38 7 73 1.38%

Warehouse 135 197 24 357 6.73%

Grand Total 1,942 2,814 548 5,303

Percent 36.61% 53.06% 10.33%

The statewide economic potential for nonresidential AES by 2024 is approximately 5.3 GW. SCE shows the

greatest economic potential, as it did with technical potential. The largest economic potential is in the

large office sector, which is quite different from the leading sector for technical potential of single family

residential. Changes in residential rates or the ability to monetize a number of other value streams (e.g.,

firming of renewables and ancillary services for grid support), or faster than expected cost reductions,

could cause the residential sectors to be cost effective.

Estimated AES Market Growth and Potential

The actual market potential is a fraction of economic potential. An EPRI energy storage potential study54

stated that “Although it is difficult to predict how consumers will respond to the relatively new energy

storage technologies, the analysis assumes that the mid-range of the energy efficient product adoption

rate (35%) is a reasonable proxy for direct customer adoption of energy storage systems.” We therefore

estimate market potential to be 35% of economic potential or 1,856 MW.

Using the 1,856 MW market potential, we can fit past energy storage installations to an adoption S-curve

to estimate market growth in the future. Figure 6-26 shows actual installations and market growth based

on this market potential. This curve shows AES meeting the 2024 AB 2514 goal for behind the meter

energy storage 4 years early—in early 2020.

54 Rastler, D, Electricity Energy Storage Technology Options. A White Paper Primer on Applications, Costs, and Benefits, EPRI,

1020676, December 2010; This study based market potential on technical not economic potential. However, the May 12, 2008 California Energy Efficiency Potential Study found market sizes of 27 to 41% of economic potential; in the same range as the 35% used here.



Figure 6-26: AES Market Growth Forecast

To meet the forecasted growth, the AES market will have to grow quickly, but at a slower rate than has

been seen to date in the SGIP. The average year over year growth would be 28%, which is close to the

31% used in the SGIP Cost Effectiveness Model. This is also a similar rate of growth to the 29% average

growth seen in the California PV industry since the start of the CSI.55 Figure 6-27 shows the annual installs

needed to meet the cumulative forecasts in Figure 6-26.

55 Based on annual installations in the SGIP and CSI tracking databases for years 2007 through 2014



Figure 6-27: Forecasted Cumulative and Annual Year over Year Growth Rates

6.5 Historical Trends in DG and Energy Storage Growth in California

Table 6-20 presents a comparison between estimates of the technical and economic potential of DG and

energy storage technologies in California and the quantities of these resources that were installed as of

the end of 2014. With installed DG and energy storage capacities making up less than 1% of the economic

potential, it is clear that there is significant opportunity to develop additional DG and distributed energy

storage capacity in California. However, these numbers also raise the question of why so little DG and

energy storage have been developed to date in California.

Table 6-20: Summary of DG and Energy Storage Potentials vs. Installed Capacities in CA56

Technology Area

2020 Technical

Potential (MW)

2020 Economic

Potential (MW)

Capacity Installed

in CA at 2014 (MW)

Percent of

Economic Potential

CHP 24,025 17,900 287 1.6%

Wind 16,166 16,166 25 < 0.2%

Biogas 593 539 33 6%

Energy storage 14,940 5,303 4 < 0.1%

Totals: 54,658 39,460 349 < 1%

To better understand operation of the DG and energy storage markets in California, we can examine the

relationship between the growth of these technologies in the SGIP against policies impacting the SGIP and

56 Note that we have included 60 MW of all-electric fuel cells into the CHP installed capacity estimates.



the technologies. Figure 6-28 shows the growth in DG and energy storage technologies funded under the

SGIP on a timeline that also shows policies influencing the program.

Figure 6-28: Trends in DG and Energy Storage Growth in SGIP and Associated Policies

The timeline Figure 6-28 shows potential associations between growth in the DG and energy storage

resource. However, it provides little insight into the barriers and opportunities associated with developing

DG and energy storage in California and the role of the SGIP.

The information obtained from interviews with the SGIP PAs, program staff, project manufacturers and

developers, and other key stakeholders identify critical barriers and opportunities associated with

transforming California’s DG and energy storage markets and the possible role of the SGIP. While it is

evident that the SGIP has played a key role in the growth of these technologies, we still need to assess the

degree to which SGIP has influenced the market in California. To do that, we looked at other DG programs

operated outside of California. Those findings are the focus of the next section.

SGIP’S INFLUENCE ON DG MARKET TRANSFORMATION | 7-1

7 SGIP’S INFLUENCE ON DG MARKET TRANSFORMATION

Several states have implemented incentive programs, grants, and regulatory policies to encourage the

installation of DG technologies. To isolate the influence of SGIP on the installation of DG technologies in

California and in other states, this section begins by assessing the installation of DG technologies and the

implementation of incentive, grant, and regulatory policies that may potentially influence the likelihood

of implementing DG technologies in eight comparison states from 1999 through 2014. This section of the

report will focus on nine states: California (CA), Connecticut (CT), Illinois (IL), Massachusetts (MA),

Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA), and Wisconsin (WI). These states were

chosen for analysis and comparison to DG installations in California because they represent states in

different phases of DG development for the technologies included in the analysis. Some of these states

are just beginning to develop a DG policy. These states provide information on the market without policy

intervention. Other states have advanced DG policies. Including states with advanced DG policies helps to

compare the potential influence of SGIP with the influence of other policies in states that have prioritized

the implementation of DG.

This section of the report focuses on the installed capacity of small CHP, wind, and landfill and dairy and

swine digester projects and the programs and policies that may have influenced the implementation of

these types of DG. The focus on these technologies is due to their eligibility in SGIP and the availability of

historical, state specific information on the installed capacity of these technologies. Capacity information

was derived from the following sources: CHP capacity data (US DOE Combined Heat and Power Installation

Database); wind capacity data (USGS Wind Installation Database); landfill gas to energy capacity

information (US Environmental Protection Agency (USEPA) Landfill Methane Outreach Program); and,

Dairy and Swine Digester data (USEPA Livestock Anaerobic Digester Database). The section does not

include an analysis of the installed capacity of energy storage technologies due to the lack of available

data on historic, state-specific information on the installed capacity of energy storage.

The information on programs and policies was gathered from the Database of State Incentives for

Renewables & Efficiency (DSIRE) database.1 The DSIRE data consist of information on programs and

policies related to renewables for a variety of “Implementation Sectors.” In addition to the state and

program name, the DSIRE data include a number of fields to group programs and summary descriptions

of each program’s history and its relevance to different technologies. In most cases, the data include

information on the relevant time period associated with the program.

The first subsection of this chapter presents information on the qualitative relationships between policies

and growth (or lack of growth) in CHP, wind and biogas capacity by state and year. However, it is difficult

to determine from this qualitative approach the extent to which the SGIP or other state and utility policies

have influenced growth in CHP, wind or biogas markets. In the second subsection of this chapter, we use

a statistical approach to determine if there are discernable impact of SGIP on these markets and to

determine the impacts of other programs on installed capacity. The last subsection compares the practices

used by Program Administrators in other states who run DG support programs similar to the SGIP. The

1 These data are available from the DSIRE database located on the North Carolina Clean Energy web site:

http://www.dsireusa.org/



intent is to identify if there are practices in other states and programs that could help improve DG market

transformation activities in California.

Assessing Influence through Indicators

CHP Programs, Policies, and Accomplishments

Using data from the US DOE Combined Heat and Power Installation Database,2 Figure 7-1 illustrates the

CHP capacity installed in California (CA), Connecticut (CT), Illinois (IL), Massachusetts (MA), Michigan (MI),

New Jersey (NJ), New York (NY), Pennsylvania (PA), and Wisconsin (WI). The data are disaggregated by

state and by type of CHP prime mover technology. The data are limited to installations of 5 MWs or less

of capacity that are fueled by natural gas or fuel oil. These data represent CHP technology likely to be

located behind the customer meter and of a size consistent with eligibility for SGIP. Technologies fueled

with biogas are analyzed separately.

The data in Figure 7-1 clearly indicates that California has installed substantially more CHP capacity

between 1999 and 2014 than other states included in this analysis. New York has installed approximately

43% of the capacity installed in California. The data show that reciprocating engines (IC engines) represent

the largest share of capacity for both California and New York. Wisconsin has a small amount of

microturbine CHP capacity, but the installed capacity is substantially smaller than for other states such

that it appears that Wisconsin has no capacity.

2 These data are available from the DOE’s web site: https://doe.icfwebservices.com/chpdb/. The data collection effort is

funded by the U.S. DOE and maintained by ICF international. The data provides a comprehensive listing of CHP projects in the U.S. For this analysis, CHP installations have been limited to 5 MW or less. As with all large data collection efforts of this type, it is possible that some CHP installations in the U.S. are missing from this data source. Given the nature of these data, it is possible that information on CHP capacity specific to SGIP may differ from the data in the DOE dataset. For consistency across all states, the analysis uses the DOE data for all states’ CHP capacity information.

https://doe.icfwebservices.com/chpdb/



Figure 7-1: State Level CHP Accomplishments from 1999 to 2014

CHP in California: Programs and Policies

Figure 7-2 summarizes CHP capacity sized 5 MW and less installed in California from 1999 to 2014. The

chart also identifies CHP related programs and policies implemented during this time period in California.

The data on the CHP programs and policies were derived from the DSIRE database. Figure 7-2 illustrates

that the installed capacity of CHP increased substantially after 2001. The 2002 increase in the installed

capacity of CHP occurred at the same time the Self-Generation Incentive Program began providing

incentives, the Public Leadership Solutions for Energy or PULSE program begin providing loans, and the

Energy Financing Industrial Development Bond Program began. These programs were instituted, at least

in part, to help California develop capacity during and following the state’s energy crisis of 2000 and 2001.3

These programs were quickly followed by policies on Net Metering and the development of a California

Renewable Portfolio Standard.

3 In the summer of 2000 and continuing through early 2001, California experienced severe rolling blackouts that affected

much of the state. For example, see http://www.pbs.org/wgbh/pages/frontline/shows/blackout/california/timeline.html

-

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

CA CT IL MA MI NJ NY PA WI

Reciprocating Engine

Other

Microturbine

Fuel Cell

Combustion Turbine

Combined Cycle

Boiler/Steam Turbine



In 2006 the California legislature revised the SGIP by limiting incentives to only fuel cells and wind turbines

starting January 1, 2008.4 The decline in newly installed CHP capacity observable in Figure 7-2 from 2009-

2011 is likely due, at least in part, to the elimination of SGIP incentives and their impact on customers’

decisions to implement CHP. It is also likely that the recession of 2008 negatively impacted the installation

of CHP capacity. In 2012, SGIP eligibility for CHP to receive incentives was restored and the level of CHP

installations increased. The timeline of SGIP and the yearly changes in CHP installed capacity in California

support the importance of SGIP in customers’ decisions to install CHP.

Figure 7-2: CHP California: Programs and Policies

CHP in Connecticut: Programs and Policies

Figure 7-3 shows the amount of CHP capacity 5 MW and less installed in Connecticut from 1999 to 2014.

The chart also overlays the timing of CHP related programs and policies implemented during this same

time period. The fall in CHP capacity observed between 2012 through 2014 may be associated with net

metering provisions that drew away potential customer investment in CHP. As shown in Figure 7-3,

Connecticut implemented many new policies designed to assist the installation of CHP beginning in 2003,

including the Connecticut Clean Energy Fund (CCEF) Project 150 Initiative, which had a goal to install 150

MW of clean energy. The CCEF rebated many fuel cell projects.

4 Assembly Bill 2778, Lieber, February 24, 2006. The technologies had to meet emission standards established by the

California Air Resources Board’s Distributed Generation certification standards.



Figure 7-3: CHP in Connecticut: Programs and Policies

The numerous policies implemented from 2003 to 2007 likely contributed to the substantial growth in

installed CHP capacity observed from 2007 through 2009. It is possible that the recession of 2008 and the

completion of the 150 MW CCEF goal impacted new installed capacity in 2010.

In 2011 Connecticut implemented Net Metering and the CCEF Alpha Project. The CCEF Alpha Project was

designed to fund the development and implementation of clean energy technologies. CHP was one of

several options within the Alpha Project. Net metering in Connecticut requires the state’s investor-owned

utilities to net meter “Class 1” renewable-energy resources including solar, wind, landfill gas, fuel cells,

sustainable biomass, ocean-thermal power, wave or tidal power, low-emission advanced renewable-

energy conversion technologies, and hydropower facilities up to 2 MW.5 In 2012 and 2013, Connecticut

expanded the number of programs designed to facilitate the implementation of CHP technologies though

there was a drop in CHP installations between 2011 and 2012. As noted earlier, the fall in capacity in 2012

through 2014 may be associated with a potential negative impact of net metering on CHP by essentially

drawing interest towards other technologies that benefited by net metering provisions.

CHP in Illinois: Programs and Policies

Figure 7-4 illustrates the amount of CHP capacity 5 MW and less installed in Illinois from 1999 to 2014 and

the CHP related programs and policies implemented during this time period. Illinois implemented the

5 Information on net metering in Connecticut was derived from information provided on the clean energy authority web site:

http://www.cleanenergyauthority.com/solar-rebates-and-incentives/connecticut/connecticut-net-metering/.

http://www.cleanenergyauthority.com/solar-rebates-and-incentives/connecticut/connecticut-net-metering/



Clean Energy Community Foundation Grants in 1999. As shown in the chart, customers in Illinois installed

approximately 24 MW of CHP capacity from 1999 to 2003.

The data also shows that from 2004 to 2013 Illinois installed approximately 5 MW of new CHP capacity.

During this time period, Illinois began a net metering policy and a renewable portfolio standard. Illinois’

net metering policy qualified residential and commercial electric generators up to 2 MW if the generation

of electricity was generally for the customer’s use and if the generator was powered by photovoltaics,

wind, dedicated crops grown for electricity generation, anaerobic digestion of livestock or food processing

waste, fuel cells or microturbines powered by renewable fuels, or hydroelectricity. The CHPs analyzed in

this section of the report are those fueled by natural gas and oil, and would generally have been ineligible

for Illinois’ net metering policy. The ineligibility of traditional CHP for net metering may have made CHP

relatively less cost-effective when compared with other forms of distributed generation that were eligible

for net metering in Illinois. In 2014, Illinois passed the Public Sector Combined Heat and Power Pilot

Program in an attempt to increase the CHP capacity in public sector buildings. The ICF CHP data, however,

did not indicate that this policy had led to the installation of any capacity in 2014. Given the long planning

times required for CHP projects, the Public Sector CHP Pilot Program may lead to increased CHP capacity

in the future.

Figure 7-4: CHP in Illinois: Programs and Policies



CHP in Massachusetts: Programs and Policies

The amount of CHP installed from 1999 to 2014 in Massachusetts is shown in Figure 7-5 along with CHP

related programs and policies implemented during this time period. Massachusetts implemented a

Renewable Portfolio Standard (RPS) in 2002 and an Alternative Energy Portfolio Standard (AEP) in 2008.

The Massachusetts RPS mandated a minimum percentage of electricity sales had to come from renewable

resources including solar, wind, small hydro, landfill and anaerobic digester gas, marine or hydrokinetic

energy, geothermal energy or eligible biomass fuel. The Massachusetts AEP provided an opportunity for

businesses, institutions, and government to receive incentives for installing CHP, flywheel storage, coal

gasification, and efficient steam technologies. The AEP obligation was initially set at 1% of electricity

supplies in 2009, increasing by 0.5% points each following year until 2014; at which time the growth rate

would be reduced to 0.25% per year.6 The Massachusetts CHP installed capacity data in Figure 7-5

indicates that while the Massachusetts RPS may not have led to substantial increase in CHP capacity, the

AEP appears to be positively associated with an increase in CHP capacity in Massachusetts.

Figure 7-5: CHP in Massachusetts: Programs and Policies

6 Information on Massachusetts’ RPS and AEP were derived from http://www.mass.gov/eea/energy-utilities-clean-

tech/renewable-energy/rps-aps/rps-and-aps-program-summaries.html.

http://www.mass.gov/eea/energy-utilities-clean-tech/renewable-energy/rps-aps/rps-and-aps-program-summaries.html




CHP in Michigan: Programs and Policies

Figure 7-6 shows the amount of CHP capacity 5 MW and less installed in Michigan from 1999 to 2014 and

CHP related programs and policies implemented during this time period. Michigan implemented an

Alternative Energy Personal Property Tax Exemption in 2002. This law exempted industrial and

commercial property from increases in taxes if increases in property value were due to improvements

associated with energy efficiency or distributed generation additions. Michigan also enacted a Refundable

Payroll Tax Credit for businesses that locate in a NextEnergy Zones and conduct research and development

or manufacture alternative energy technologies. It also enacted a Nonrefundable Business Activity Tax

Credit that provided tax credits for businesses engaged in alternative energy research, development, and

manufacturing. From the data presented in Figure 7-6, none of these programs appear to have had a

substantial impact on the amount of CHP installed capacity in Michigan. Based on the CHP data in Figure

7-6, the installation of new CHP capacity appears relatively random, with positive capacity installed in one

year and then one or two years with no new CHP capacity installed.

Figure 7-6: CHP in Michigan: Programs and Policies

CHP in New Jersey: Programs and Policies

The amount of CHP installed in New Jersey sized at 5 MW and less from 1999 to 2014 is shown in Figure

7-7. CHP related programs and policies implemented during this time period also are so shown in the

chart. New Jersey implemented a net metering policy and a Societal Benefits Charge in 1999. The New



Jersey net metering policy was not applicable to CHP fueled by natural gas or oil. In 1999 and 2000 New

Jersey installed approximately 5 MW of CHP capacity (based on the criteria for this analysis) each year. In

2001 New Jersey implemented a Renewable Portfolio Standard and the New Jersey Renewable Energy

Incentive Program. While fuel cells fueled by biomass are eligible for these programs, traditional CHP

fueled by natural gas and oil are typically not eligible. In New Jersey, new CHP capacity was relatively small

in 2001 but grew to approximately 5 MW of new CHP capacity in 2002.

During the 2008-2012 time period New Jersey implemented several new programs and policies designed

to encourage the implementation of CHP technologies. High priority programs and policies included

property tax exemptions in 2008, the Clean Energy Solutions Capital Investment Loans and Grants and the

Edison Innovation Clean Energy Fund in 2009, the ARRA Clean Energy Solutions CHP program in 2010, and

the Clean Energy Solutions for Large and Small CHP and Fuel Cells in 2012. The passage of multiple

programs and policies in New Jersey from 2008 to 2012 appears to have led to a more consistent level of

CHP capacity installation. In addition, the Fuel Cell programs implemented in 2012 appear to have led to

an increase in fuel cell installed capacity in 2013.

Figure 7-7: CHP in New Jersey: Programs and Policies

CHP in New York: Programs and Policies

Figure 7-8 shows the amount of CHP capacity 5 MW and less installed in New York from 1999 to 2014

along with CHP programs and policies implemented during this time period. New York implemented an

Interconnection Policy in 1999 to establish rules and regulations governing customer’s ability to connect



electric generators to the grid. In 2000 and 2001, New York’s installed CHP capacity grew from

approximately 2 MW in 1999 to over 8 MW in each of the following two years. In 2002 New York

implemented a Fuel Cell Rebate and Performance Incentive, though their CHP capacity continued to be

dominated by reciprocating (IC) engines. A substantial increase in fuel cell capacity occurred in 2006.

Since 2000, the installation of new CHP capacity in New York has exceeded 6 MW per year for every year

except 2011. Several programs and policies have been adopted since the Fuel Cell Program in 2002 to

support the continued growth of CHP in New York. New York implemented a Renewable Portfolio

Standard in 2004, while a customer-sited tier of the New York RPS is focused on supporting customer sited

smaller-scale renewables including fuel cells. In 2009 NYSERDA began a CHP and Renewable Generation

Technology Assistance program. In 2011 New York also implemented several programs designed to

increase the installed capacity of CHP. In 2011 the Anaerobic Digester Gas-to-Electric Rebate and

Performance Incentive Program and the Distributed Generation Combined Heat and Power Program were

instituted. These programs, combined with the NYSERDA’s Operating Plan for Technology and Market

Development Programs have helped to increase the capacity of CHP installed in New York since 2011. In

2011 the New York Public Service Commission (PSC) reaffirmed its decision to continue to use the System

Benefits Charge to help fund the Technology and Market Development Portfolio (T&MD). The mission of

the T&MD fund is to “test, develop, and introduce new technologies, strategies and practices that build

the statewide infrastructure to reliably deliver clean energy to New Yorkers.”7 In New York, through the

T&MD and other programs, substantial funds have been provided for CHP development and installation.

As illustrated in Figure 7-8, the yearly installed capacity of CHP 5 MW and smaller in New York has grown

every year since 2011. The newly installed CHP capacity was approximately 5 MW in 2011, growing to

over 18 MW of newly installed CHP capacity in 2014.

7 “Operating Plan for Technology and Market Development Programs (2012-2016)”, second revision 2/15/2013, case 10-m-

0457, NYSERDA.



Figure 7-8: CHP in New York: Programs and Policies

CHP in Pennsylvania: Programs and Policies

The amount of CHP capacity 5 MW and less installed in in Pennsylvania from 1999 to 2014 along with CHP

related programs and policies implemented during this time period is shown in Figure 7-9. Pennsylvania

implemented a large number of programs and policies from 1999 to 2001 applicable to CHP. These policies

included utility specific programs associated with sustainable energy fund loans and grants and a

statewide Renewable Portfolio Standard. Following the implementation of these policies, there was a

small increase in CHP installations from 2003 through 2005.

In 2005 Pennsylvania implemented an Alternative Energy Portfolio Standard (AEPS). Pennsylvania’s AEPS

was designed to encourage reliance on more diverse and environmentally friendly sources of energy. The

energy requirements under Pennsylvania’s AEPS included different tiers or classifications for alternative

technologies: Tier 1, Tier 2, and solar specifications. The AEPS specifies a requirement for the share of

energy allowed in each tier to satisfy the AEPS standard. Pennsylvania’s AEPS fuel cells are a Tier 1

resource and general distributed generation systems are a Tier 2 option. Pennsylvania also implemented

net metering and Interconnection Standards in 2006. In 2007 Pennsylvania experienced a small increase

in the installation of CHP resources. In 2009, Pennsylvania instituted the Green Energy Load Fund, which

provides financing for high-performance energy systems in existing nonresidential buildings. In 2011

through 2013, Pennsylvania experienced a substantial increase in installed CHP capacity, though 2014

installation fell far short of those in the preceding years.



Figure 7-9: CHP in Pennsylvania: Programs and Polices

CHP in Wisconsin: Programs and Policies

Figure 7-10 shows the amount of CHP capacity 5 MW and less installed in Wisconsin from 1999 to 2014

along with CHP related programs and policies implemented during this time period. Figure 7-10 illustrates

that Wisconsin has had very little CHP installed capacity and few programs or policies specific to CHP. In

2009 Wisconsin implemented a Renewable Portfolio Standard requiring the state to obtain 10% of its

electricity from renewable sources by 2015. Wisconsin met their RPS goal in 2013. However, CHP fueled

by oil or natural gas does not contribute to satisfying Wisconsin’s RPS. The programs and policies

implemented in Wisconsin were designed to encourage electricity generation using alternative fuels and

generally do not apply to CHP fueled by more conventional fuels.



Figure 7-10: CHP in Wisconsin: Programs and Policies

Wind Programs, Policies, and Accomplishments

Using data from the USGS Wind Installation Database,8 Figure 7-11 shows the different capacity (in MWs)

of wind energy systems installed in California (CA), Connecticut (CT), Illinois (IL), Massachusetts (MA),

Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA), and Wisconsin (WI). The data are

limited to installations with 7.5 MWs or less of capacity per wind site by year of installation for the years

1999 to 2008 and 9 MW or less for years 2009-2013.9 The data is designed to represent wind installation

8 These data are available from the USGS’s web site: http://pubs.usgs.gov/ds/817/downloads/. This data set provides

industrial-scale onshore wind turbine locations in the United States through July 22, 2013, corresponding facility information, and turbine technical specifications. The wind turbine data was created through a process that synthesized existing information and combined existing data with the Federal Aviation Administration’s Digital Obstacles File (DOF) (Federal Aviation Administration, 2013). The USGS data also uses information from databases developed by the U.S. Energy Information Administration (U.S. Energy Information Administration, 2013) and the no longer maintained Wind Energy Data and Information (WENDI) data set from the Oak Ridge National Laboratory.

9 The analysis also removed capacity associated with Wethersfield Wind Farm. The capacity associated with this customer

was found to represent capacity that was not behind the customer’s meter. During the data development phase, the analysis team reviewed sites with more than 7 MW of capacity installed in a single year. Wind installations without capacity information were eliminated from eligibility for the analysis. Many of the wind installations without capacity data in the USGS data, though not all of them, were associated with site names that were clearly large scale wind farms and not eligible for this analysis due to their size and in-front of the meter status.

http://pubs.usgs.gov/ds/817/downloads/



behind the customer’s meter. Given that there is no clear indication that a wind turbine installation is

behind the customer meter, the two capacity restrictions are designed to restrict installations to those

that are likely to be behind the customer meter. During the data development phase, we reviewed the

name of customer sites in the USGS data in an attempt to limit installations to customer sited wind

turbines. The data represent capacity installations from 1999 to 2013.

The data illustrated in Figure 7-11 clearly indicates that Massachusetts has installed the most behind-the-

meter wind capacity during the analysis period, followed by California. These data also indicate that

Connecticut had not installed any behind-the-meter wind capacity from 1999 to 2013.

Figure 7-11: Wind Capacity by State

Wind in California: Programs and Policies

Figure 7-12 shows the amount of small-sized, behind-the-meter wind capacity installed in California from

1999 to 2013. The chart also overlays wind energy related programs and policies implemented during this

time period. Data on the wind programs and policies were derived from the DSIRE database. As

represented in Figure 7-12 installation of small wind capacity occurred primarily in two distinct time

periods: one from 2003 to 2005 and one from 2009 through 2012. Small wind capacity installed in the

2003 to 2005 time period represents individual installations of a single turbine each year. The capacity

-

10

20

30

40

50

60

70


MW

of

Win

d C

apac

ity



installed in the later time period represents installations at 15 sites with many sites installing multiple

turbines.

In 2001 California implemented the Solar and Wind Energy System Credit, a state tax credit valued at 7.5%

to 15% of the net, nonrebated cost of the system for systems sized up to 200 kW. California also

implemented an Energy Financing Industrial Development Bond that allowed solar and wind

manufacturers to borrow money exempt from federal taxes and a Public Leadership Solutions for Energy

(PULSE) fund to help finance renewable energy installations on public buildings. The Self-Generation

Incentive Program was also implemented in 2002 to provide incentives for the installation of distributed

generation technologies, including wind, that could help provide electricity resources at time of the

system peak. These early programs may have contributed to the early installations of wind, but the later

wind installations represent substantially more capacity and customers.

In 2009 the California legislature passed Assembly Bill 45 authorizing counties to adopt ordinances to

provide for the installation of small wind systems (50 kW and smaller) outside urbanized areas but within

the county’s jurisdiction. The bill allows counties to be more lenient than the ordinance, but establishes

maximum restrictions that a county may implement. During 2007 the SGIP was modified to provide only

incentives to wind and fuel cells. The SGIP restrictions were in place until 2012. These restrictions may

have provided additional funds for wind projects that no longer needed to compete for funds with a wider

array of distributed generation technologies. The increase in installed capacity from 2009 through 2012 is

timed with the implementation of the county wind ordinance and the modifications of SGIP.

Figure 7-12: California Wind Capacity: Programs and Policies



Wind in Illinois: Programs and Policies

Figure 7-13 illustrates the small-sized, behind-the-meter, wind capacity installed in Illinois from 1999 to

2013 and the timing of wind related programs and policies implemented during this time period. Illinois

has implemented many programs and policies designed to encourage the development of small wind

capacity. The Small Wind Grant Program was established to provide grants for wind projects between 1

and 50 kW while the Wind Energy Production Program provided financial assistance for the development

of new wind projects up to 500 kW. According to the USGS data, the first, small-sized behind-the-meter

wind capacity installed in Illinois were two sites in 2005. The joint capacity across these first installations

was 2.31 MW.

In 2007 Illinois developed net metering protocols. Illinois’ net metering applies to renewable distributed

generation with an operating capacity of less than 2 MW and intended primarily to offset the electric

requirements of the owner of the generation. Small, behind-the-meter wind in Illinois is eligible for net

metering. In 2008 Illinois established standard interconnection requirements for distributed generation

systems including wind. In 2009 Illinois established a maximum setback limit for wind turbines designed

for onsite energy generation. The rule was amended in 2013 to allow municipalities to prohibit wind

devices with a capacity of 100 kW or greater. The initial intent of the energy setback standards was to

standardize and simplify the location of wind turbines to encourage their installation while the latter

amendment limits the installation of larger (i.e., greater than 100 kW) wind turbines.

In 2009 there was a substantial increase in new, small wind capacity in Illinois. This increase is consistent

with the anticipated impact of the net metering, interconnection, and the original intent of the energy

setback standards. Illinois also saw installations of new wind capacity in 2010, 2011, and 2012. During this

time period, Illinois implemented the Large-Distributed Solar and Wind Grant Program providing

incentives for nonresidential customers installing wind as well as the Solar and Wind Rights Policy

prohibiting homeowners’ associations from preventing homeowners from installing solar or wind

systems.



Figure 7-13: Illinois Wind Capacity: Programs and Policies

Wind in Massachusetts and Programs and Policies

Data on the amount of small, behind-the-meter, wind capacity installed in Massachusetts from 1999 to

2013 is shown in Figure 7-14. In 2000 Massachusetts implemented the Commonwealth Wind Program.

The Program was designed to offer communities and developers assistance in site assessments, feasibility

studies, and development grants for appropriately-sited wind energy development.10 Massachusetts also

implemented a Renewable Portfolio Standard in 2002, obligating electricity suppliers to provide their

customers with 1% of their energy from renewable sources in 2003. In 2008 the RPS was broken into Class

1 and Class 2 obligations with Class 1 obligations increasing 1% point annually. Wind energy qualifies as a

Class 1 resource for the Massachusetts RPS.11

In 2007 and 2008 Massachusetts implemented several policies designed to encourage wind energy

development. The Massachusetts Green Communities Act set in motion many policies designed to shift

the state’s energy policy and energy consumption. The Green Communities Act expanded the RPS by

10 Information on Massachusetts’ Commonwealth Wind Program was found on the Mass CEC’s web site.

http://www.masscec.com/programs/commonwealth-wind-program.

11 Information on Massachusetts’ RPS was found on the Mass.gov Energy and the Environmental Affairs web site.

http://www.mass.gov/eea/energy-utilities-clean-tech/renewable-energy/rps-aps/rps-and-aps-program-summaries.html.

http://www.masscec.com/programs/commonwealth-wind-program




providing customers the ability to own and benefit from energy producing technologies, and allowing

customers to take advantage of net metering programs. The Model As-of-Right Zoning Ordinance was

developed by the Department of Energy Resources to assist cities and towns in establishing reasonable

standards for wind-power development.12 Massachusetts also adopted interconnection standards in 2007

mandating that all distributed generation installations must be reviewed to qualify for interconnection.

The many policies implemented in 2007 and 2008 appear to have contributed to a substantial growth in

wind energy capacity from 2009 to 2012.

Figure 7-14: Massachusetts Wind Capacity: Programs and Policies

Wind in Michigan: Programs and Policies

Figure 7-15 shows the amount of small, behind-the-meter, wind capacity installed in Michigan from 1999

to 2013 and the timing of wind energy related programs and policies implemented during this time period.

Michigan has implemented many programs and policies associated with wind energy, but few small,

behind-the-meter wind systems have been installed. The wind installations depicted in Figure 7-15

represent multiple turbines installed at two customer sites. The installation in 2001 occurred prior to the

implementation of high priority wind energy programs and policies. The wind installation in 2006 occurred

following the implementation of tax policies, Michigan’s interconnection regulations and the

development of net metering. In 2006, Michigan also implemented Renewable Energy Renaissance Zones

12 Information on Massachusetts’ model zoning ordinances for wind was found on the Mass.gov web site.

http://www.mass.gov/eea/docs/doer/green-communities/grant-program/wind-model-bylaw-mar-2012.pdf.

http://www.mass.gov/eea/docs/doer/green-communities/grant-program/wind-model-bylaw-mar-2012.pdf



created around a facility that generates renewable energy. The designation of a Renewable Energy

Renaissance Zone eliminates the customer’s requirement to pay state education taxes, personal and real

property taxes and local income taxes. Since 2006, Michigan has implemented many policies applicable

to wind systems, but no additional small, behind-the-meter systems have been installed.

Figure 7-15: Michigan Wind Capacity: Programs and Policies

Wind in New Jersey: Programs and Policies

The amount of small, behind-the-meter, wind capacity installed in New Jersey from 1999 to 2013 and

wind related programs and policies is shown in Figure 7-16. New Jersey has implemented many programs

and policies associated with wind energy, but few small, behind-the-meter wind systems have been

installed. Prior to the installation of a substantially sized wind energy system in 2006, New Jersey

implemented net metering, a Renewables Portfolio Standard, and Interconnection regulations. From 2007

to 2011 New Jersey implemented the Clean Energy Solutions Capital Investment Loan/Grant Program,

Edison Innovation Clean Energy Fund, developed permitting laws and instituted new tax regulations.

However, only one additional wind system was implemented in 2012.



Figure 7-16: New Jersey Wind Capacity: Programs and Policies

Wind in New York: Programs and Policies

Figure 7-17 shows the amount of small wind capacity installed in New York from 1999 to 2013 overlaid

with wind energy related programs and policies implemented during this time period. New York has

implemented programs and policies associated with wind energy, but few small, behind-the-meter wind

systems have been installed. In 1999 New York implemented interconnection regulations and in 2002 the

New York State Research and Development Authority (NYSERDA) developed Guidance for Local Wind and

Energy Ordinances. The Guide provides information to help communities address issues they may

encounter with the development of wind systems. In 2004 New York developed a Renewable Portfolio

Standard and in 2006 NYSERDA instituted a wind incentive program. To help encourage a network of wind

energy installers, the NYSERDA program provides incentives to eligible installers. The installers are

directed to pass through the incentives to the owners of the wind system.13 In 2009 New York

implemented net metering and in 2011 remote net metering. Remote net metering allows a

nonresidential customer to apply the excess energy produced by their system to another meter on their

account. The remote net metering system allows the nonresidential customer to potentially receive a

higher value from their excess energy production.14 In 2010 and 2012, New York experienced the

13 Information on the NYSERDA Wind Incentive Program is derived from data available on the DSIRE database.

http://programs.dsireusa.org/system/program/detail/956.

14 Information on New York’s Net Metering and Remote Net Metering policies were found on NYSERD’s web site.

http://www.nyserda.ny.gov/Cleantech-and-Innovation/Power-Generation/Net-Metering-Interconnection.

http://programs.dsireusa.org/system/program/detail/956

http://www.nyserda.ny.gov/Cleantech-and-Innovation/Power-Generation/Net-Metering-Interconnection



installation of their first small, behind-the-meter wind capacity since 2002. In 2012, NYSERDA

implemented an On-Site Wind Incentive Program providing incentives for installation of on-site or behind-

the-meter wind systems.

Figure 7-17: New York Wind Capacity: Programs and Policies

Wind in Pennsylvania: Programs and Policies

Data on small, behind-the-meter, wind capacity installed in Pennsylvania from 1999 to 2013 and wind

energy related programs and policies implemented during this time are shown in Figure 7-18.

Pennsylvania’s utilities implemented a number of wind energy related programs and policies from 1999

to 2004, but no small, behind-the-meter wind systems were installed according to the USGS wind capacity

data. Pennsylvania also implemented a Renewable Portfolio Standard in 2001, an Alternative Energy

Portfolio Standard in 2005, and net metering in 2006. A fairly significant wind energy system was installed

in 2011. However, these regulations appear to have had little overall impact on behind-the-meter wind

systems in Pennsylvania.



Figure 7-18: Pennsylvania Wind Capacity: Programs and Policies

Wind in Wisconsin: Programs and Policies

Wisconsin instituted a number of small wind energy related programs and policies beginning in 1999.

However, as the wind capacity data shown in Figure 7-19 illustrates, there have been few installations of

small, behind-the-meter wind systems. Wisconsin developed Interconnection Standards for behind-the-

meter generation in 2004, a Green Power Purchasing agreement in 2006 and a Renewable Portfolio

Standard (RPS) in 2009. Wind systems are an eligible source of renewable power under Wisconsin’s RPS.

In 2012, the Renewable Energy Competitive Incentive Program was also implemented to encourage the

installation of renewable sources of energy. To date, the Renewable Energy Competitive Incentive

Program has largely funded PV installations.15

15 Information on the distribution of incentives within the Wisconsin Renewable Energy Competitive Incentive Program were

derived from https://focusonenergy.com/business/renewable-energy.

https://focusonenergy.com/business/renewable-energy



Figure 7-19: Wisconsin Wind Capacity: Programs and Policies

Biogas Programs, Policies, and Energy Accomplishments

We use data from the US Environmental Protection Agency Landfill Methane Outreach Program and the

Livestock Anaerobic Digester Database in determining installed capacity of biogas projects. Figure 7-20

shows the capacity (in kWs) of biogas fueled generation projects installed in California (CA), Connecticut

(CT), Illinois (IL), Massachusetts (MA), Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA),

and Wisconsin (WI). The data are limited to installations with 5 MWs or less of capacity per biogas site by

year of installation for the years 1999 to 2014. The data is designed to represent capacity fueled by biogas

installation behind the customer’s meter.

The EPA data indicate that California, closely followed by Michigan, has the highest level of new biogas

capacity installed from 1999 to 2014. However, New York’s and Wisconsin’s newly installed biogas

capacity are also very similar and not substantially less than California and Michigan’s new capacity. The

newly installed capacity in Connecticut and New Jersey are substantially less and are restricted to landfill

gas capacity. Wisconsin has the largest new dairy and swine digester capacity.



Figure 7-20: New Landfill and Dairy and Swine Digester Biogas Capacity by State (1999-2013, kW)

Biogas in California: Programs and Policies

Figure 7-21 shows the amount of small (less than 5 MW) landfill and dairy and swine digester biogas

capacity installed in California from 1999 to 2014. The figure is overlaid with biogas energy related

programs and policies implemented during this time period.16 As shown in Figure 7-21, the capacity of

new biogas projects was relatively high in 1999. California has had long running programs designed to

capture the gases created by landfills. Capturing these gases helps to limit the likelihood of fires or

explosions at California landfills. According to the EPA Landfill Methane Outreach Program database, the

first landfill gas to energy project was installed in California in 1982. By 1995 California had 56 landfill gas

recovery facilities; 14 collecting landfill gas and 42 collecting landfill gas and producing energy.17

16 The data on biogas programs and policies were derived from the DSIRE database.

17 The historic information on landfill gas energy production in California was derived from the CA.Gov web site.

http://www.energy.ca.gov/biomass/landfill_gas.html.

-

20,000

40,000

60,000

80,000

100,000

120,000


KW

of

Bio

Gas

Cap

acit

y

LandFill

Digester

http://www.energy.ca.gov/biomass/landfill_gas.html



Figure 7-21: California Biogas Capacity: Programs and Policies

A number of policies potentially impacting biogas to energy project development have been enacted in

California. In 1996 California passed the Public Benefits Fund for Renewables and Energy Efficiency. The

Public Benefits Fund provided early funding for biogas fueled on-site generation and likely contributed to

the growth in capacity from 1999 to 2012. In 2000, California instituted Interconnection Standards and

the Solar Energy and Distributed Generation Grant Program to encourage on-site generation. California’s

energy crisis of 2000 and 2001 encouraged the state to implement additional policies and programs to

encourage on-site generation to help reduce peak generation. The new programs include the Self

Generation Incentive Program, Public Leadership for Energy (PULSE) Loans and the Energy Financing

Industrial Development Bond Program. The substantial increase in biogas capacity following the

implementation of these programs is illustrated in Figure 7-21.

In 2003 California also implemented net metering, the Agricultural Biomass to Energy Program and the

California Renewable Portfolio Standard; all programs and policies designed to increase the

implementation of renewable energy. At the utility level, the SCE Biomass Standard Contract is a resource

procurement process through which SCE contracts with biomass projects in their territory with 20 MW or

less of capacity. In 2006 the California legislature revised the SGIP by limiting incentives to only fuel cells

and wind turbines starting January 1, 2008. The substantial decline in new biogas capacity observed from

2010 to 2011 may be due to changing rules of the Self-Generation Incentive Program combined with the

economic recession of 2008 and 2009. California experienced an upturn in new biogas capacity from 2012

through 2014. California has a long history of facilitating the use of biogas to produce energy. This history,



however, illustrates the importance of maintaining a partnership between biogas energy producers and

state programs to preserve the productive use of these resources.18

Biogas in Connecticut: Programs and Policies

Connecticut has only begun recently to use landfill gas to produce electricity. The amount of small biogas

energy capacity installed in Connecticut from 1999 to 2014 is shown in Figure 7-22. The first landfill gas

recovery project in Connecticut occurred in 1998. Figure 7-22 illustrates that new installations of biogas

capacity in Connecticut occurred primarily from 2007 through 2009 and that all biogas projects were

associated with landfills.

Connecticut has instituted several programs and policies designed to increase the capacity of renewable

energy production. In 2003 the Connecticut Clean Energy Fund (CCEF) Project 150 Initiative was enacted

with a goal of installing 150 MW of clean energy. The DPUC Capital Grants and Low-Interest Loans for

Customer-Side Distributed Resources and the On-Site Renewable DG Program were designed to stimulate

demand for behind-the-meter installations of renewable energy at nonresidential facilities in Connecticut.

Connecticut also implanted a Renewable Portfolio Standard in 2006 and net metering in 2011. In 2013

Connecticut also implemented an RFP for Renewable Power Produced by Biomass, landfill Gas, and Run-

of-River Hydropower to help meet Connecticut’s Class 1 renewable energy requirements within

Connecticut’s RPS.

18 See http://www.energy.ca.gov/biomass/biomass.html for a description of the history and current state of biomass energy

production in California.

http://www.energy.ca.gov/biomass/biomass.html



Figure 7-22: Connecticut Biogas Capacity and Programs and Policies

Biogas in Illinois: Programs and Policies

Illinois has a medium-sized but significant dairy industry with over 690 licensed dairies, making it the 22nd

largest milk producing state in the country.19 In addition, Illinois was ranked as the fourth largest producer

of pork products in 2011.20 As of 2014, Illinois had 39 active landfills.21 The amount of small biogas to

energy projects installed in Illinois from 1999 to 2014 and biogas energy related programs and policies

implemented during this time is shown in Figure 7-23. The data illustrates that all biogas project

installations involved landfill gas other than a digester biogas installation in 2002 and a very small digester

biogas installation in 2008.

Illinois has a history of using landfill gas to produce electricity, with the first landfill gas project occurring

in 1988. Illinois Clean Energy Community Foundation Grants, implemented in 1999, provide grants for

renewable energy projects including anaerobic digester installations. Illinois’ Renewable Portfolio

Standard was implemented in 2007 and landfill gas and energy from anaerobic digestion is eligible under

the RPS standard. In 2007 Illinois implemented net metering regulations and in 2008 Illinois established

19 From https://www.midwestdairy.com/farm-life/dairy-in-the-midwest/

20 From http://www.ilpork.com/?104

21 From http://www.epa.illinois.gov/topics/waste-management/landfills/landfill-capacity/2014/index



Interconnection Standards. Additional landfill gas projects have been completed in Illinois since 2011,

however, these projects exceed the 5 MW limit for this analysis.

Figure 7-23: Illinois Biogas Capacity: Programs and Policies

Biogas in Massachusetts: Programs and Policies

Massachusetts has a relatively small diary industry with 137 dairy farms, primarily family run operations.22

Similarly, Massachusetts has a fairly small swine industry, ranking 37th in pork production in 2004.23

Massachusetts has developed policies governing recycling and disposal of solid wastes and, as of 2014,

there were only 17 landfills in the state accepting household and business wastes.24 Figure 7-24 shows the

amount of new and small landfill, dairy and swine digester biogas capacity installed in Massachusetts from

1999 to 2014 along with biogas energy related programs and policies implemented during this time

period. The chart shows that all biogas project installations represent landfill gas to energy production

other than digester biogas projects in 2011 and 2013.

22 From http://semaponline.org/resources/dairy-farming/

23 From http://www.stuffaboutstates.com/agriculture/livestock/hogs.htm

24 From http://www.mass.gov/eea/docs/dep/recycle/solid/swminma.pdf; see page 17 for a description of landfill operations

http://www.mass.gov/eea/docs/dep/recycle/solid/swminma.pdf



Figure 7-24: Massachusetts Biogas Capacity: Programs and Policies

Massachusetts’ first landfill gas to energy installation occurred in 1995. Massachusetts implemented a

Renewable Portfolio Standard in 2002 and capacity fueled by landfill gas or digester gas is eligible for Class

1 qualified generation. In 2007 Massachusetts implemented a Green Power Purchasing Commitment,

which directs state government agencies to purchase 15% of annual electricity consumption from

renewable sources by 2012 and 30% by 2020. In 2011, Massachusetts developed the Green Communities

Grant Program to help communities meet the five criteria required to become a Green Community and

qualify for grants to finance additional renewable energy projects. The first criterion for a Green

Community is to provide as-of-right siting in designated locations for renewable/alternative energy

generation, research and development, or manufacturing facilities.25 Through these programs,

Massachusetts is developing policy to expand its capacity of electricity generated from landfill and

digester biogas.

Biogas in Michigan: Programs and Policies

Michigan has a robust dairy industry with over 1,900 dairy herds; ranking it 6th in milk production in the

country as of 2014.26 Michigan also has a significant swine industry, with over 2,600 swine farms.27 In

25 Information on Massachusetts Green Communities Grant Program was gathered from the Mass.gov web site.

http://www.mass.gov/eea/energy-utilities-clean-tech/green-communities/gc-grant-program/

26 From http://www.milkmeansmore.org/local-milk/dairy-facts

27 From http://i2.wp.com/www.mipork.org/wp-content/uploads/2015/03/MI-Economic-2_SM_Version.jpg

http://www.mass.gov/eea/energy-utilities-clean-tech/green-communities/gc-grant-program/



2010, Michigan had approximately 70 to 80 landfills.28 Figure 7-25 shows the amount of new small dairy,

swine digester and landfill biogas capacity installed in Michigan from 1999 to 2014. The data shows that

while the majority of the newly installed capacity was fueled with landfill gas, new capacity fueled by

digester biogas was substantial.

Michigan has a history of assisting with biogas to energy project development. Michigan’s first landfill gas

to energy project occurred in 1986. Michigan implemented several tax regulations for renewable

generation in 2002 and formalized Interconnection Standards in 2003. In 2006, Michigan implemented

the Biomass Gasification and Methane Digester Property Tax Exemption. That policy exempts energy

production related to farm facilities from real and personal property taxes. In 2006 the city of Ann Arbor

set a goal of 30% renewable energy for all municipal operations by 2010. While Ann Arbor fell short of the

2010 goal, the city passed a new resolution in 2011 to reach the 30% renewables goal by 2015.29 Michigan

also implemented net metering and a Renewable Energy Standard in 2008. The success of these policies

is observable in the newly installed capacity illustrated in Figure 7-25.

Figure 7-25: Michigan Biogas Capacity: Programs and Policies

28 From http://www.michigan.gov/documents/deq/DEQ-OWMRP-SWS-SolidWasteAnnualReportFY2014._481071_7.pdf.

29 Information on Ann Arbors Green Communities objects was found at energy.gov. http://energy.gov/savings/city-ann-arbor-

green-power-purchasing

http://energy.gov/savings/city-ann-arbor-green-power-purchasing

http://energy.gov/savings/city-ann-arbor-green-power-purchasing



Biogas in New Jersey: Programs and Policies

New Jersey has a dairy industry composed on over 110 dairy farms.30 The state’s swine operations are

very small with less than 1,000 pigs.31 New Jersey has over 400 registered landfills.32 Figure 7-26 shows

the amount of new small landfill, dairy and swine digester biogas capacity installed in New Jersey from

1999 to 2014 as well as biogas related programs and policies implemented during this time. As the data

shows, all new biogas capacity in New Jersey occurred after 2000 and all of the newly installed biogas

capacity was fueled with landfill gas.

New Jersey’s first landfill gas to energy project was installed in 1987. In 2000 New Jersey developed net

metering regulations and in 2001 implemented a Renewable Portfolio Standard in which landfill and

biomass gas are Class 1 renewable energy sources. New Jersey also implemented a Renewable Energy

Incentive Program in 2001 to promote the installations of renewable electric generation. In 2004 New

Jersey witnessed a substantial increase in installed new capacity from biogas projects. The recession in

2008 and 2009 may help explain the lack of biogas projects during 2009 and 2010 but the state was moving

forward, implementing many programs to help encourage clean energy solutions.

Figure 7-26: New Jersey Biogas Capacity: Programs and Policies

30 From http://www.nj.gov/agriculture/news/hottopics/Topics050104.html

31 From http://nationalhogfarmer.com/site-

files/nationalhogfarmer.com/files/archive/nationalhogfarmer.com/images/NHF_SOIReport__2011.pdf

32 From http://cdn.intechopen.com/pdfs-wm/27162.pdf; see page 158

http://cdn.intechopen.com/pdfs-wm/27162.pdf



Biogas in New York: Programs and Policies

New York has a vibrant and growing dairy industry, with over 5,000 family-owned farms making it the 4th

ranked milk producing state in the nation.33 New York’s swine industry ranked 30th in pork production in

2010.34 At the end of 2014, there were 27 active MSW landfills in New York State.35

Figure 7-27 shows the amount of new and small landfill, dairy and swine digester biogas capacity installed

in New York from 1999 to 2014. The data indicates that the newly installed biogas capacity is dominated

by landfill gas energy projects but that New York also has substantial new capacity derived from digester

biogas.

New York’s first landfill gas to energy project was installed in 1982. In 1999 New York instituted

Interconnection Standards to help facilitate the connection of behind-the-meter energy production to the

electric grid. New York passed a Renewable Portfolio Standard in 2004 and electricity produced from

landfill or digester gas qualifies as a renewable resource. In 2009 NYSERDA passed the CHP and Renewable

Generation Technical Assistance Program to help cost-share the technical assistance studies needed prior

to the implementation of renewable generation projects. New York also implemented net metering in

2009. In 2012 substantial resources supporting the installation and operation of new Anaerobic Digester

Gas to Electricity systems using dairy farm wastes were made available by NYSERDA. The programs and

regulations implemented by New York and NYSERDA are designed to encourage the continued expansion

of the use of biogas to fuel electricity production.

33 From http://www.nyanimalag.org/an-overview-of-nys-dairy-industry/


35 From http://www.dec.ny.gov/chemical/23682.html



Figure 7-27: New York Biogas Capacity: Programs and Policies

Biogas in Pennsylvania: Programs and Policies

Pennsylvania has a growing dairy industry with nearly 7,400 dairy farms, making it the 5th ranked milk

producing state in the nation in 2014.36 With over 100,000 pigs, Pennsylvania ranked as the 12th largest

pork producer in the nation in 2010.37 Pennsylvania has 46 active landfills.38

Figure 7-28 shows the amount of new and small-sized landfill, dairy and swine digester biogas capacity

installed in Pennsylvania from 1999 to 2014. As shown in the chart, the newly installed biogas capacity is

dominated by landfill gas projects. However, Pennsylvania also has substantial new capacity derived from

dairy and swine digester biogas.

The Pennsylvania Department of Environmental Protection identifies Pennsylvania landfill methane

projects; with 37 landfill projects listed as having either closed or operational landfill methane projects as

of 2011, one landfill with a project in development, eight landfills as candidates and only five landfills as

36 From http://centerfordairyexcellence.org/pennsylvania-dairy-industry-overview/


38 From http://www.pareportcard.org/PDFs/Solid%20Waste%20FINAL%20NATL.pdf



undetermined. Pennsylvania’s methane landfill projects appear to have a mix of electricity production and

direct use of high BTU landfill gas to be blended into existing natural gas pipelines.39

Pennsylvania’s utilities have enacted a number of grant and loan programs designed to encourage the

development of renewable energy and the state passed a Renewable Portfolio Standard in 2002. In

Pennsylvania, biologically derived methane gas and biomass energy are Tier 1 alternative energy sources.

Pennsylvania also developed Interconnection Standards and net metering regulation in 2006 that apply

to electricity produced by biogas if the nonresidential customer’s system is less than 3 MW.

Figure 7-28: Pennsylvania Biogas Capacity: Programs and Policies

Biogas in Wisconsin: Programs and Policies

Wisconsin has 10,290 dairy farms and nearly 1.3 million dairy cows, ranking it as the 2nd largest milk

producing state in the nation.40 Similarly, with over 45,000 pigs, Wisconsin ranked 16th in pork production

in the nation in 2010.41

39 Information on Pennsylvania’s landfill methane gas projects is from the Pennsylvania state web portal.

http://www.portal.state.pa.us/portal/server.pt/community/landfill_methane_outreach_program/14091/pa_landfill_methane_projects/589657

40 From http://www.wmmb.com/assets/images/pdf/WisconsinDairyData.pdf






The amount of new small-sized landfill, dairy and swine digester biogas capacity installed in Wisconsin

from 1999 to 2014 is shown in Figure 7-29. Landfill gas projects dominate the biogas capacity installed in

Wisconsin but there has also been substantial new capacity derived from dairy and swine digester biogas.

The new installed capacity of dairy and swine biogas digesters is largest from 2007 through 2013.

Figure 7-29: Wisconsin Biogas Capacity: Programs and Policies

Wisconsin’s first landfill gas to energy projects occurred in 1986. Wisconsin also has an extensive number

of dairy and swine biogas digester projects, highlighting the importance of Wisconsin’s dairy economy.

In 1999 Wisconsin instituted a Public Benefits charge and created Focus on Energy to raise money to fund

energy efficiency, low-income programs, and renewable energy programs. In 2004 Wisconsin developed

Interconnection Standards designed to regulate the interconnection of distributed generation 15 MW and

less to the utility grid. In 2006 We Energies developed a special tariff for energy developed from biogas

resources to be sold back to the utility. The biogas buy-back rate is similar to net metering but designed

specifically for customers using biogas to produce electricity. The buy-back program works to improve the

cost effectiveness of distributed generation systems.

In 2009 Wisconsin implemented a Renewable Portfolio Standard, mandating that 10% of electricity be

derived from renewable sources including biogas. In 2012 Focus on Energy developed the Renewable

Energy Competitive Incentive Program (RECIP). RECIP provides incentives to cost-effective renewable

energy systems through a competitive RFP process including systems fueled by biogas. The consistent



increase in landfill and digester biogas projects in Wisconsin illustrates Wisconsin’s interest in using the

state’s biogas resources to produce energy

Statistical Analysis of CHP, Wind, and Biogas Capacity

This section of the report focuses on the statistical evaluation of the influence of programs and policies

on installed capacity. The analysis continues to analyze nine states: California (CA), Connecticut (CT),

Illinois (IL), Massachusetts (MA), Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA), and

Wisconsin (WI). The analysis are divided into the three technology groupings reviewed above: CHP, wind,

and biogas fueled capacity.

The CHP capacity data is derived from the U.S. DOE Combined Heat and Power Installation Database. The

development of these data is sponsored by the U.S. DOE and maintained by ICF International. While the

database tracks CHP installations of all sizes, the analysis for this report is limited to installations with 5

MWs or less of capacity that are fueled by natural gas or fuel oil. These data represent CHP technology

likely to be behind-the-meter and a size consistent with eligibility for SGIP. CHP technologies fueled with

bio-gas are analyzed separately.42

The wind capacity data is derived from the U.S.G.S. Wind Installation Database. The data include onshore

wind turbine locations and sizes through July 22, 2013. Turbine information from the Federal Aviation

Administration Digital Obstacle File were combined with facility information from databases developed

by the U.S. Energy Information Administration and the no longer maintained Wind Energy Data and

Information data set from Oak Ridge National Laboratory as the primary sources of data for the database.

For this analysis, these data are limited to installations with 7.5 MWs or less of capacity per wind site by

year of installation for the years 1999 to 2008 and 9 MW or less for years 2009-2013.43 The data is designed

to represent wind installation behind the customer’s meter. Given that there is no clear indication that a

wind turbine installation is behind the customer meter, the two capacity restrictions are designed to

restrict installations to those that are likely to be behind the customer meter.

The landfill gas to energy capacity information is derived from the US Environmental Protection Agency

(USEPA) Landfill Methane Outreach Program. The Dairy and Swine Digester data is from the USEPA

Livestock Anaerobic Digester Database. The data are limited to installations with 5 MWs or less of capacity

per biogas site by year of installation for the years 1999 to 2014. The data is designed to represent capacity

fueled by biogas installation behind the customer’s meter.

42 Information on the U.S. DOE CHP database is available at a joint DOE ICF web site.

https://doe.icfwebservices.com/chpdb/about . The database relies on self-reporting of CHP installations and may not be comprehensive or may need data updates. These data, however, represent a comprehensive set of yearly new capacity data for CHP installations across the U.S. Incompleteness that may occur in the data is likely to be unbiased across technologies and states.

43 The analysis also removed capacity associated with Wethersfield Wind Farm. The capacity associated with this customer

was found to represent capacity that was not behind the customer’s meter. During the data development phase, the analysis team reviewed sites with more than 7 MW of capacity installed in a single year. Wind installations without capacity information were eliminated from eligibility for the analysis. Many of the wind installations without capacity data in the USGS data, though not all of them, were associated with site names that were clearly large scale wind farms and not eligible for this analysis due to their size and in-front of the meter status. Information on the wind database is available from the USGS web site: http://pubs.usgs.gov/ds/817/ .

https://doe.icfwebservices.com/chpdb/about

http://pubs.usgs.gov/ds/817/



Given that the installed capacity shows substantial differences in magnitude from state to state and year

to year, the modeled dependent variable was the log transformation of the installed capacity.

Development of Independent Variables

The primary source for the independent variables was the Database of State Incentives for Renewables &

Efficiency (DSIRE). The DSIRE data contain information for a large number of programs. As shown in Table

7-1, there is information on 362 unique nonfederal programs for the nine focus states. For each

technology, the objective was to transform the DSIRE program information into a set of independent

variables with a potentially predictive relationship with the annual installed capacity in each state. The

idea was that by controlling for these program impacts across all states, it would be possible to estimate

the influence of the SGIP on installed capacity above and beyond what would have happened due to other

programs and policies. Given the large amount of data, however, this transformation called for multiple

steps, which are described below.

Table 7-1: Count of Programs in DSIRE Data by State and Implementation Sector

Implementation Sector

State Local Nonprofit State Utility All

CA 29 0 26 8 63

CT 2 1 32 9 44

IL 4 1 24 2 31

MA 1 1 40 5 47

MI 7 0 24 2 33

NJ 0 0 26 0 26

NY 3 0 40 5 48

PA 13 0 19 3 35

WI 4 0 22 9 35

All 63 3 253 43 362

The first step was to assess all the programs to determine their relevance to the technologies in question.

For example, the “We Energies - Biogas Buy-Back Rate” in Wisconsin would have no obvious influence on

installed wind capacity. Likewise, there were many programs that were relevant to wind but not biogas

or CHP. This called for a rigorous review that included research beyond the information in the DSIRE data

to find descriptions of the program that would clarify each program’s scope in terms of technologies.

Additionally, this research helped to populate or refine the information on the start and end dates of

many programs, which is a critical element for determining a program’s influence.

Other than general categorization to allow some differentiation, all programs in the DSIRE data are treated

equally, irrespective of level of incentive. In reality, this is highly unlikely, so as the second step, the

programs were flagged as “primary drivers,” meaning those programs that were known to be of more

relevance. This step was conducted by a small team of subject matter experts with decades of experience



in renewable energy. While not a purely objective exercise, this step was necessary to further reduce the

DSIRE data to a manageable and theoretically more meaningful set of programs and policies.

After the first two steps to reduce the programs in the DSIRE data down to those relevant to the

technologies in question with primary relevance, the original set of programs was reduced substantially.

These counts are shown in Table 7-2, where the total number of wind programs was 125. Biogas and CHP

programs were reduced more substantially, with 88 and 89 programs, respectively, across three

implementation sectors.

Table 7-2: Count of Primary Driver Programs by State and Technology

Implementation Sector

Local State Utility All

State Bio CHP Wind Bio CHP Wind Bio CHP Wind Bio CHP Wind

CA 0 0 2 8 9 14 2 1 1 10 10 17

CT 0 0 0 13 18 15 0 1 0 13 19 15

IL 0 0 2 7 5 14 1 1 2 8 6 18

MA 0 0 0 5 7 8 0 1 0 5 8 8

MI 1 0 2 10 9 13 0 0 0 11 9 15

NJ 0 0 0 8 13 9 0 0 0 8 13 9

NY 0 0 0 7 6 8 1 1 3 8 7 11

PA 10 9 12 6 4 9 0 0 0 16 13 21

WI 0 0 1 8 4 10 1 0 0 9 4 11

All 11 9 19 72 75 100 5 5 6 88 89 125

The next step was to take these programs and reduce them to a set of variables that represent the effects

of different types of programs. To be clear, the objective of this analysis was not to identify the influence

of individual programs, but rather to model the influence of different program types across states. That

is, the objective was not to assess the effect of program A in state J, but rather to control for the effects

of programs A, B, and C, which represent some category of program, in states J, K, L, etc. Controlling for

the influence of the various programs and policies allows for the isolation of the influence of the SGIP

program.

One challenge with this step is determining the appropriate level aggregation for establishing program

types. In terms of level of granularity, at one end there is the DSIRE “program type” field and at the other

the DSIRE “program category,” which is simple grouping of programs into financial incentives versus

regulatory policy. After examination of the data (i.e., looking at the counts of different program types by

state over time), it was clear that defining variables by the 32 DSIRE program types would be too granular,

with far too many variables to easily specify models and interpret the results. At the same time, the

program category would be too general, with nearly identical presence across all the states, which would

have made it impossible for a statistical model to isolate the different effects. As a result, a map was

created with three alternate levels of aggregation with Summary Level 1 (11 variables), Summary Level 2

(8 variables), and Summary Level 3 (6 variables) levels to create and test different variable configurations.

The mapping is shown in Table 7-3.



Table 7-3: Mapping of DSIRE Program Types to Different Aggregation Levels

DSIRE Program Type Summary Level 1 Summary Level 2 Summary Level 3

DSIRE Program

Category

Bond Program Financing Financing Financing Financial Incentive

Building Energy Code Code and Standard Policy Other Policy Other Regulatory Policy

Climate & Environment Policy Other Policy Other Policy Other Regulatory Policy

Corporate Depreciation Tax Tax Tax Financial Incentive

Corporate Tax Credit Tax Tax Tax Financial Incentive

Corporate Tax Deduction Tax Tax Tax Financial Incentive

Corporate Tax Exemption Tax Tax Tax Financial Incentive

Energy Efficiency Resource Standard

Policy Other Policy Other Policy Other Regulatory Policy

Energy Standards for Public Buildings

Code and Standard Policy Other Policy Other Regulatory Policy

Feed-in Tariff Policy Other Policy Other Policy Other Regulatory Policy

Grant Program Grant Grant Incentive Financial Incentive

Green Building Incentive Incentive Other Incentive Incentive Financial Incentive

Green Power Purchasing Green Power Policy Other Policy Other Regulatory Policy

Industry Recruitment/Support

Grant Grant Incentive Financial Incentive

Interconnection Net Energy Metering (NEM)

NEM NEM Regulatory Policy

Line Extension Analysis Policy Other Policy Other Policy Other Regulatory Policy

Loan Program Financing Financing Financing Financial Incentive

Net Metering NEM NEM NEM Regulatory Policy

Other Incentive Incentive Other Incentive Incentive Financial Incentive

PACE Financing Financing Financing Financing Financial Incentive

Performance-Based Incentive Incentive Incentive Incentive Financial Incentive

Personal Tax Credit Tax Tax Tax Financial Incentive

Property Tax Incentive Tax Tax Tax Financial Incentive

Public Benefits Fund Incentive Other Incentive Other Policy Other Regulatory Policy

Rebate Program Incentive Incentive Incentive Financial Incentive

Renewables Portfolio Standard

RPS RPS RPS Regulatory Policy

Sales Tax Incentive Tax Tax Tax Financial Incentive

Solar Renewable Energy Credit Program

Incentive Other Incentive Incentive Financial Incentive

Solar/Wind Access Policy Policy Other Policy Other Policy Other Regulatory Policy

Solar/Wind Contractor Licensing

Policy Other Policy Other Policy Other Regulatory Policy

Solar/Wind Permitting Standards

Permitting Policy Other Policy Other Regulatory Policy

Utility Rate Discount Policy Other Policy Other Policy Other Regulatory Policy

The objective in using different levels of aggregation is twofold. First, it is necessary for simplification of

the model specification. Second, it is necessary to define variables that represent a theoretical influence

of programs or policies across the different states. This influence does not have to be present in every

state, but it is important that they apply to more than just one so that they did not just represent a state

specific impact. In practice, most of the key programs (NEM, Renewable Portfolio Standards, etc.)



maintained their individuality, but there are enough other programs to justify the use of different types

of aggregation.

The substantial number of programs in the DSIRE data meant that in many cases there were multiple

programs associated with a single level of aggregation. While the years in which they were active could,

and usually did vary, there were still many years where multiple programs were relevant at the same time.

This meant that there were two options for creating the independent variables. One option was simply to

define dummy variables, where a 1 represents the presence of any program. The other was to define the

variables as the sum of programs that are active in a given time period. For example, if one program

started in 2004 and another started in 2006, the variable would be equal to 1 for 2004 and 2005 but

increase to 2 in 2006.

Because the goal of the analysis is to assess any effects of the SGIP, it is important to note that the program

is excluded from all of the other independent variables. This raises the question of how to represent the

program’s impacts in the model and many different strategies were used. The most straightforward

approach for modeling SGIP was the use of a single dummy variable, set to 1 in the state of California

during the program’s active years starting in 2002 and 0 otherwise.

However, as discussed previously, the specific mechanism through which SGIP incentivized the adoption

of different technologies changed over time, making its effect in different time periods inconstant. To

account for this, another option was to use multiple dummy variables reflecting different “eras” of the

program. One example of this, and the approach that was ultimately deemed most accurate, was the use

of a dummy variable for the years 2002 to 2007, another for 2008 to 2010, and a third for 2011 to the

present. Another question in assessing the impacts was whether to configure the variables to account for

influence in other states, so dummy variables representing the program years or eras could be interacted

with the different states.

We also explored lagged program and policy impacts as well as a number of other ways of specifying how

impacts might manifest themselves. For example, program start dates were often in the middle of the

year, so we looked at configuring variables that did not include that partial year at all or included that

partial year only when the program was in effect for more than eight months. We also looked at variables

where the effects started only in the second year of the program. Additionally, another hypothesis might

be that programs have an influence only in the first X years, when they get the “low hanging fruit,” so we

looked at variables where the dummy/count was only counted for a limited horizon. The upshot is that

these different variable configurations did not dramatically affect the results, so we went with the models

that were the most “stable” and easy to explain. It’s likely that some programs and policies would have

worked better with lagged effects or with effects that did not persist. However, with the sheer number of

programs, it simply would not have been possible to look at program-specific impact configurations

efficiently.

Model Specifications

The final analysis set was created by merging the series of installed capacity for each technology with its

respective set of independent variables. This data set presented an opportunity and a challenge in that it

permitted thousands of different alternate model specifications. The main considerations in determining

the most appropriate model specification included:



» Level of aggregation of dependent variables.

» Implementation sector(s) to include (state, utility, and local).

» Fixed effects (state and year).

» Binary (dummy) or count-based variables.

» SGIP specification (single dummy, era dummy variables, etc.).

» Time frame (15 or 20 years).

At the outset of the analysis, thousands of different models were estimated to assess these various

considerations. Automated review of the model outputs and other diagnostics helped reduce the options

to a more manageable subset based on decisions that could be determined more objectively. For example,

the 15-year time frame consistently generated higher indicators of model fit (R2). Additionally, the

inclusion of fixed effects for both state and year in the final model specification was determined to be

superior based on the substantial number of statistically significant parameter estimates associated with

the impacts and the much higher indicators of model fit for models that included those effects. This latter

decision was supported by visualization of the data that showed clear differences by state and over time

in the dependent variables that were highly unlikely to be causally related with the independent variables.

Other decisions were less objective, depending more on professional judgment. For example, for the CHP

model, the final SGIP specification was to use three dummy variables, which was based on knowledge of

how the program changed over time and that the impacts were not likely to be constant over the analysis

time frame. For wind, in contrast, there was no theoretical reason for assuming any era-specific impacts,

so a single SGIP dummy variable was employed in the final model.

The criteria for selection of the final model specification included the model fit, the presence of intuitive

and statistically significant parameter estimates for key variables, and a lack of variables that would lead

to serial correlation and volatile parameter estimates.

CHP Results

The final model for estimating SGIP’s impact on CHP – presented in EQUATION 1 below – included state-

level variables on the count of programs based on the most granular level of aggregation, utility and local

level variables for regulatory policy and financing, and a single variable to capture the count of NEM

programs across state, utility, and local jurisdictions. The model was a two-way fixed effects model by

state and year estimated for the 15 years from 2000 to 2014.44

44 A two way fixed effects model includes dummy variable for each state and each year. The year fixed effect controls for

influences on capacity that are similar across all of the states for a specific year but can vary from year to year. These types of effects would include federal policies and the macro-economy. The state fixed effect controls for influences on capacity that are fixed over time but specific to a state. These types of effects could include state or utility policies that were enacted prior to the analysis period.



Equation 1: Final Model for CHP

𝑙𝑛(𝑘𝑊)𝑠𝑡 =∝𝑡+ 𝛾𝑠 + 𝛽1 × 𝐶𝑆𝐹𝑖𝑛𝑎𝑛𝑐𝑒𝑠𝑡+ 𝛽2 × 𝐶𝑆𝐺𝑟𝑎𝑛𝑡𝑠𝑡+ 𝛽3 × 𝐶𝑆𝐺𝑟𝑒𝑒𝑛𝑃𝑜𝑤𝑒𝑟𝑠𝑡+ 𝛽4 ×𝐶𝑆𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑠𝑡+ 𝛽5 × 𝐶𝑆𝑁𝐸𝑀𝑠𝑡+ 𝛽6 × 𝐶𝑆𝑃𝑜𝑙𝑖𝑐𝑦𝑂𝑡ℎ𝑒𝑟𝑠𝑡+ 𝛽7 × 𝐶𝑆𝑅𝑃𝑆𝑠𝑡+ 𝛽8 × 𝐶𝑆𝑇𝑎𝑥𝑠𝑡 + 𝛽9 ×𝐶𝑈𝑅𝑒𝑔𝑃𝑜𝑙𝑖𝑐𝑦𝑠𝑡+ 𝛽10 × 𝐶𝐿𝐹𝑖𝑛𝑎𝑛𝑐𝑒𝑠𝑡+ 𝛽11 × 𝑃𝑟𝑖𝑐𝑒𝑠𝑡 + 𝛽12 × 𝑆𝐺𝐼𝑃0207𝑠𝑡+ 𝛽13 ×𝑆𝐺𝐼𝑃0811𝑠𝑡+ 𝛽14 × 𝑆𝐺𝐼𝑃1214𝑠𝑡+𝜀

Where:

Ln(kW)st is the natural log of the installed capacity in state s and year t

∝𝑡 is the time fixed effect for year t

𝛾𝑠 is the state fixed effect for state s

CSFinancest is the Count of State Financing Programs in state s and year t

CSGrantst is the Count of State Grant Programs in state s and year t

CSGreenPowerst is the Count of State Green Power Programs in state s and year t

CSIncentivest is the Count of State Incentive Programs in state s and year t

CSNEMst is the Count of State and Utility NEM Policies in state s and year t

CSPolicyOtherst is the Count of State Policy Other in state s and year t

CSRPSst is the Count of State RPS in state s and year t

CSTaxst is the Count of State Tax Programs in state s and year t

CURegPolicyst is the Count Utility Regulatory Policy in state s and year t

CLFinancest is the Count of Local Financing in state s and year t

Pricest is the average industrial electricity price in cents per kWh in state s and year t

SGIP0207st is a dummy variable for the time period 2002 to 2007 for California

SGIP0811st is a dummy variable for the time period 2008 to 2011 for California

SGIP1214 st is a dummy variable for the time period 2012 to 2014 for California

β1 – β14 are the estimated coefficients associated with the independent variables

and ε is the error

This final CHP model resulted in an R2 of 0.72 (F = 6.88, p. <.0001), indicating that the variables in the

model accounted for 72% of the variability in the installed capacity. While this represents a relatively good

fit for the model, of more interest are the parameter estimates, particularly those associated with SGIP as

presented in Table 7-4.



Table 7-4: Parameter Estimates for Model of SGIP CHP Impacts

Variable

Parameter

Estimate Standard Error t Value Pr > |t|

Intercept 1.387 3.15866 0.44 0.6615

SGIP 2002 to 2007 4.459 2.16769 2.06 0.0423

SGIP 2008 to 2011 4.150 2.47835 1.67 0.0972

SGIP 2012 to 2014 3.710 2.62342 1.41 0.1604

Count State Financing 0.094 0.89423 0.10 0.9167

Count State Grant 0.206 0.48256 0.43 0.6710

Count State Green Power 2.021 2.23236 0.91 0.3675

Count State Incentive -1.196 1.56004 -0.77 0.4453

Count All NEM -1.411 0.67003 -2.11 0.0377

Count State Policy Other -1.004 0.67154 -1.49 0.1382

Count State RPS 0.480 0.71472 0.67 0.5038

Count State Tax 0.848 1.49728 0.57 0.5723

Count Utility Regulatory Policy 0.104 1.37013 0.08 0.9394

Count Local Financing 5.440 1.98115 2.75 0.0072

Industrial Price 0.316 0.35262 0.90 0.3719

With respect to the non-SGIP variables in the model, only net metering (NEM) and the count of local

financing policies and programs were statistically different from zero with a 95% confidence. The

estimated impacts of these two variables were mixed in terms of their signs.45 The model found that local

financing programs had a positive, statistically significant impact on installed capacity. In contrast, NEM

policies were found to result in a statistically significant decrease in CHP. For all other variables, the signs

were mixed and none were statistically significant.

For most of the variables in the model, there were no a priori expectations about what the signs should

be for the parameter estimates. While all of the programs in the DSIRE data were related to CHP, they

also had relevance to other technologies, so in some cases a program could take finite funds away from

CHP for other investments. NEM is perhaps one of the more clear cases of this, where the implementation

of a NEM policy could lead to investment resources going to technologies that were more likely to have

the opportunity to sell generation back to utilities. Implementation of NEM could lead to more investment

in PV and a reduction in investment in CHP due to the NEM improving the relative cost effectiveness of

PV.

45 Statistical significance can be determined in Table 7-4 using the last column. If the value in the last column is less than 0.1

then the estimated coefficient is statistically different from zero with a 90% confidence.



As for the parameter estimates for SGIP impacts, the first era from 2002 to 2007 generated a statistically

significant parameter estimate with a 95% confidence, indicating that SGIP led to a statistically significant

increase in the installed capacity of CHP in California from 2002 to 2007. The estimated parameter on the

second era of SGIP, from 2008 to 2011, is positive and statistically significant with a 90% confidence. All

three parameter estimates for SGIP are positive and their slight decline over time is consistent with

expectations about changes in SGIP’s incentives for CHP would have led to a diminishing influence on

installed capacity.

Using the log installed capacity as the dependent variable was necessary given the distribution of the

untransformed capacity values, but it makes the direct interpretation of the parameter estimates less

straightforward. As one way of interpreting the results, Table 7-5 shows how the three parameter

estimates translate into estimates of average annual installed capacity (kW) in California that is influenced

by SGIP for the three SGIP eras. These data indicate that the average annual estimated installed capacity

influenced by SGIP is largest during the first time period when SGIP provided incentives to many types of

technologies and smallest during the second SGIP era when SGIP incentives were limited to fuel cells. The

estimated average annual installed capacity influenced by SGIP rises during the third SGIP era (2012-2014)

when the SGIP expanded the list of eligible CHP technologies. The second column of data is the yearly

average actual installed CHP capacity in California as derived from the DOE data. The last column of data

compares the SGIP influenced installed capacity to the actuals. SGIP is estimated to have influenced the

installation of nearly 94% of the installed CHP capacity in California from 2002 to 2007, 78% from 2008 to

2011, and by 2012 to 2014, the share had declined to 67%. During the first SGIP era, the program is

estimated to have influenced a large share of installed capacity as the program incentivized many

technologies and likely helped to expand the market’s knowledge and awareness. During the second SGIP

era, the program is estimated to have influenced over 75% of a much smaller installed capacity. The

influence during the second era may represent actual incentivized projects and a maintenance of the

knowledge and awareness developed during the first era.

Table 7-5: California CHP Capacity and Estimated Influence of SGIP

SGIP “Era”

Average Annual Estimated

Installed Capacity

Influenced by SGIP

Average Actual Installed

Capacity from DOE Data

Estimated SGIP as Percent

of Actual

2002 to 2007 37,025 39,565 93.6%

2008 to 2011 8,760 11,226 78.0%

2012 to 2014 13,812 20,615 67.0%

Result for Wind and Biogas

Of the hundreds of models estimated for wind and biogas capacity, there were only a few cases (for wind,

with none for biogas) where the estimated SGIP impacts were associated with statistically significant

parameter estimates. While these might imply that SGIP has not had a substantial impact on wind or

biogas capacity, these models were highly volatile or unstable, with parameter estimates changing in sign

and statistical significance with only minor changes to the model specifications or input data.

Consequently, the prudent approach is to say that the modeling was not able to produce stable estimates

of the impact of SGIP on wind or biogas capacity beyond the effects of other programs and policies.



These results, while possibly disappointing from the perspective of the program, are not totally

inconsistent with expectations given the data. In the case of wind capacity, the sparseness of installations

and variability in magnitude (both within and across states) were such that they did not lend themselves

to predictive association with any of the available DSIRE data. This can be seen in Figure 7-11 through

Figure 7-19, where most states had gaps of multiple years with no new capacity and the magnitude of

new installations showed considerable variation.

In the case of biogas, the issues with the data are less glaring compared to wind. The biogas data is more

consistent across states and time and fewer states have several years without new biogas capacity. Landfill

biogas to energy capacity, however, is driven in part by the need to meet federal clean air requirements.

Landfill gas-to-energy projects have been ongoing in many states since the early to mid-1980s. While state

and local policy may have some impact on the timing of new capacity installations, many of the

installations appear to be more influenced by federal policy and the availability of landfills that are

candidates for landfill gas to energy projects. While it is likely that state and local policies have a larger

impact on dairy and swine digester biogas capacity, this capacity was overwhelmed in the analysis by the

landfill biogas capacity. Given the dominance of landfill gas capacity in the model and the importance of

federal regulations combined with landfill candidates, it is not surprising that models of biogas capacity

did not lead to the development of a stable model emphasizing the importance of state, local, and utility

policies.

Comparisons of SGIP to other DG and Energy Storage Programs –

Program Administrator Surveys in the Northeast

To better understand the influence of DG and energy storage programs on the implementation of SGIP

technologies, all four current Program Administrators (PAs) for SGIP in California were interviewed, as

were select staff formally associated with SGIP and three PAs from DG and energy storage incentive

programs in New York and Massachusetts. Interviewing former SGIP staff provides historic perspective on

the SGIP as it has evolved since 2001 while interviewing PAs from New York and Massachusetts provides

comparisons with other DG and energy storage programs across the country. The interviews were

conducted as structured in-depth conversations. The conversations gathered information on the PAs’

perspective of the program goals and objectives and the programs’ progress toward meeting its’ goals;

the primary market barriers and opportunities; the utilities opportunities for growing DG and energy

storage and the barriers to growing DG and energy storage in the market.

DG and Energy Storage Program Goals

Program goals and objectives can have a substantial impact on how a program is organized, marketed,

and implemented. The PAs understanding of the program’s goals can be crucial to the program

successfully achieving its objectives. The survey of PAs responsible for DG and energy storage programs

began by asking PAs to describe their understanding of their program’s goals.

The PA from New York described their DG and energy storage program goals stressing objectives

consistent with market transformation. The DG and energy storage program goals for New York include

trying to move the market forward while also installing more projects, making the market more self-

sustaining and building more competencies with the program served technologies. The New York PA



stated that the program goal was to simplify the customer acquisition of DG and energy storage

technologies, making the installation process more efficient by eliminating misunderstandings and

mistakes. Reaching these goals helps drive costs down, thereby helping towards development of a self-

sustaining market.

In Massachusetts, cost effective energy savings is a primary goal for DG and energy storage programs.

Specific, quantitative goals are set and the PAs work to meet these goals through the strategic use of

incentives and targeting of customers and technologies. One example of this policy is the targeting of CHP

technologies installed at projects with high thermal loads throughout the year. These sites help obtain the

biggest energy savings for both the customer and the utility. While increasing customer awareness of

program served technologies is not a goal of the Northeast programs, customer and vendor education of

how targeted technologies can reduce energy operation costs is seen as an integral part of their programs.

Customer satisfaction is also not necessarily a goal, but is seen as a program outcome if the energy savings

goals are reached.

CHP Technology Drivers

In the Northeast, the PAs feel that there are a number of factors behind the rise of certain technologies.

The following factors were seen as CHP drivers:

» Reliability of technology

» Ability to understand how technology works

» Good track records

» Replicability

» High volume of installations (Prolific)

» Availability of multiple vendors

» Stable infrastructure

» Overall reasonable costs

» Justifiable technology - can compete on its own merit

» Educated Consumers

This set of drivers were repeated frequently during our interviews, often regardless of the technology

being discussed. One respondent stated the importance for emerging technologies to become more

widespread and prolific in the territory for the technology to become considered as a viable market

option. They used an analogy of snowboarding. Once snowboard was seen as a fringe sport, but as more

and more people took up the sport it became a viable option on the slopes with an entire industry and

infrastructure put in place to support its growth.

CHP Technology Barriers

A number of factors were also seen as limiting the growth of certain CHP technologies. The following

factors are seen as having a detrimental effect on the growth of CHP technologies:



» High maintenance requirements

» Skilled staff necessary to maintain and operate

» Ability of customer to defer purchase decision (discretionary)

» Aesthetic concerns (i.e. noise and vibration)

» Historically bad track record

» Specific, limited applications for optimum operation

» Long lead times and a large amount of time and resources required for purchase decision and installation

The barriers faced by the respondents were seen as program challenges which could and should be

overcome through program design and provision of incentives. For example, customer issues regarding

the noise and vibration of newly installed systems was a commonly heard complaint. These stories

resonated with customers and even though many of these issues were resolved when the correct

equipment was installed, horror stories about vibration problems continued spreading from customer to

prospective customer. The PAs elected to address these types of problems by standardizing the project

design for small scale systems based on proven approaches. By standardizing the project design, this

prevented the likelihood that project developers would make the same wrong installation mistakes over

again and again. While some installation problems still occur and lead to stories among the customer

population, new success stories are also being broadcast among prospective customers. Unfortunately,

more work needs to be done as the weight of a bad experience more than offsets a large number of

project successes.

Impact of Regulations and Policies on DG and Energy Storage Adoption

While some policies and regulations vary across the country, there are many similarities in the regulatory

environments. What often differs, however, is how the PAs and their program designs embrace these

policies and work to promote their programs within the regulatory parameters. Similarities are leading to

identification of best practices in how PAs of DG and energy storage incentive programs are approaching

implementation of their programs.

Best Practices in the Northeast:

The following are some of the best practices that emerged during our interviews with the Program

Administrators in the Northeast.

A key takeaway is the importance in having an overarching goal, such as market transformation, which

guides the direction of all programs. Secondary goals are also viewed as very important. Setting a goal,

whether it is number of installations or energy savings is critical in the operation of DG and energy storage

programs. As the market gears up and barriers are overcome and goals are met, the PAs are able to ramp

incentive levels down and move the market towards self-sufficiency.

The importance of understanding the drivers and barriers that customers contemplate when deciding

whether or not to adopt DG and energy storage technologies and incorporating these barriers and drivers

into the development of the program is critical. For instance, Massachusetts’ PA’s understand that one of

the barriers customers face is the maintenance issues related to technologies such as CHP. These



maintenance hurdles must be overcome if the program is going to be successful and reach program goals.

Rather than leaving this to each customer to work out on their own, the programs incorporate

maintenance procedures and requirements directly into the program. By requiring that customers follow

a set 3-year maintenance plan, the program not only results in less equipment failure, but also ensures

that the technology is running up to its potential. It also results in a set demand for maintenance that

allows the service market to grow to meet these maintenance needs. Another barrier is the high and often

fluctuating cost of technologies. Creating a market where at least several vendors compete helps bring

costs down and results in much more stable technology costs.

Another best practice uncovered in our research was the separation of the CHP program into two distinct

programs based on installation size. In order to help promote the goal of a self-sustaining market, this

utility realized that they needed to capitalize on the lessons learned over the past decade and document

the obtained competencies. For small to medium installations, this was done through a “prescriptive”

cataloging of technologies. While not possible for larger installations, putting together a catalog of types

of equipment installations for small to medium CHP units has proven very successful in reducing problems

with wrong-sizing of equipment. Project developers become more of a support resource and

“personalized shopper” as the vendor is now responsible for choosing the right-sized equipment for small

to medium sized installations. This new approach raises customer confidence that they are, indeed,

installing a system that has been proven to work for specific business needs.

Finally, incentives should not be the main reason that customers opt for program served technologies.

Rather, the incentives are drivers to program entry for qualified customers whose participation will not

only benefit their business, but also help the utility meet its program savings goals. In New York, the

incentives were set at a level to offset the utility interconnection fees. It is felt that these incentives helped

compensate for the “frictions” or barriers still found in the marketplace. It is hypothesized that as the

frictions are reduced and the program becomes more streamlined and efficient, that the incentives can

be scaled back. The importance of private capital investments were also mentioned as a driver in market

transformation. As barriers to technological adoption are reduced, the motivation for private capital to

enter the marketplace increase. This private capital investment should offset at least some of the

incentives that are now in place for program served technologies.

MARKET TRANSFORMATION SURVEY FORMS | A-1

APPENDIX A MARKET TRANSFORMATION SURVEY FORMS

The following pages contain images of Market Transformation customer surveys under the Self-Generation Incentive Program.



A.1 SGIP Host Customer Survey



A.2 SGIP Program Administrator Survey



A.3 SGIP Manufacturer Survey



A.4 SGIP Installer Survey



A.5 SGIP Combined Manufacturer + Installer Survey

HOST CUSTOMER SURVEY RESULTS | B-1

APPENDIX B HOST CUSTOMER SURVEY RESULTS

B.1 Site Weighted Survey Results SGIP Program Experience

Please rate your experience with the SGIP program - Overall Application Process nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Very Dissatisfied 1 0% 1 1% 1 3% 2 11% 5 2% 2 3% Dissatisfied 4 2% 1 1% 2 8% 1 16% 8 3% 3 5% Neutral 4 2% 7 12% 8 26% 1 7% 20 10% 10 18% Satisfied 9 53% 20 25% 11 41% 10 70% 4 31% 54 45% 10 18% Very Satisfied 5 36% 10 57% 5 20% 1 7% 3 46% 24 34% 8 15% NA 8 6% 3 4% 1 3% 1 4% 1 7% 14 5% 22 38% Not Answered 1 2% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%

Please rate your experience with the SGIP program - Incentive Amount nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Very Dissatisfied 1 1% 1 3% 1 7% 3 2% 1 2% Dissatisfied 4 32% 2 1% 1 5% 1 4% 8 15% 2 3% Neutral 3 1% 4 12% 6 22% 3 15% 1 16% 17 11% 8 15% Satisfied 9 23% 23 28% 10 36% 9 70% 6 55% 57 33% 11 19% Very Satisfied 7 38% 11 57% 9 33% 1 4% 1 22% 29 36% 10 19% NA 7 4% 1 2% 1 7% 9 3% 19 33% Not Answered 1 2% 1 2% 2 1% 5 9% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%



Please rate your experience with the SGIP program - Incentive Payout Time Frame nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Very Dissatisfied 3 1% 1 1% 2 8% 1 7% 7 3% 3 6% Dissatisfied 5 21% 3 4% 1 3% 1 4% 1 16% 11 11% 4 7% Neutral 5 2% 8 15% 6 20% 4 19% 2 16% 25 12% 11 20% Satisfied 7 35% 20 21% 9 40% 8 66% 4 39% 48 37% 8 14% Very Satisfied 2 34% 9 57% 7 24% 1 4% 1 22% 20 33% 7 14% NA 7 4% 1 2% 1 2% 1 7% 10 3% 20 35% Not Answered 2 2% 2 4% 4 2% 3 5% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%

Please rate your experience with the SGIP program - Program Requirements nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Very Dissatisfied 1 1% 2 8% 2 11% 5 3% 2 3% Dissatisfied 4 20% 2 2% 3 13% 2 32% 1 16% 12 16% 3 6% Neutral 6 3% 11 19% 8 23% 3 15% 1 8% 29 13% 13 23% Satisfied 7 35% 19 20% 9 37% 8 42% 5 47% 48 33% 10 18% Very Satisfied 5 36% 8 56% 4 15% 1 22% 18 31% 6 12% NA 8 5% 1 2% 1 7% 10 3% 19 33% Not Answered 1 2% 2 4% 3 2% 3 5% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%

Would you have installed the technology in the absence of SGIP funding? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No, not at all 20 41% 25 33% 13 47% 5 26% 3 40% 66 39% 21 24% Maybe, at a later time

9 57% 14 56% 9 28% 10 70% 5 42% 47 51% 23 29%

Yes, definitely 2 2% 3 6% 6 21% 1 8% 12 7% 10 15% Not Answered 2 5% 1 4% 1 4% 2 11% 6 3% 27 32% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



Technology Evaluation Please rate your level of satisfaction with each of the technologies that you installed.

nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not satified at all 1 0% 2 3% 3 11% 2 7% 8 4% 2 3% Somewhat satified 2 1% 13 15% 6 22% 2 7% 1 5% 24 10% 10 11% Satisfied 9 6% 11 15% 6 15% 6 37% 5 42% 37 15% 20 26% Very satified 11 88% 17 62% 10 41% 5 44% 4 45% 47 65% 41 50% NA 7 5% 1 6% 1 5% 9 4% 7 8% Not Answered 1 0% 3 7% 1 4% 1 8% 6 3% 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Based on your experience, how likely are you to recommend the technology to your friends? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not at all 1 0% 13 16% 10 34% 4 15% 28 13% 6 8% Very Likely 20 92% 28 74% 17 62% 12 85% 10 92% 87 80% 67 83% NA 8 6% 1 6% 1 2% 10 4% 8 9% Not Answered 2 1% 2 5% 1 3% 1 8% 6 2% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



Describe your current level awareness and knowledge of Solar PV nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 2 2% Heard about it 2 1% 2 0% Know and understand the technology

2 1% 2 3% 5 18% 1 4% 2 16% 12 6% 4 5%

Have done some research into learning about the technology

7 3% 5 8% 8 24% 5 41% 1 16% 26 14% 10 12%

Very well informed about the technology

11 40% 21 68% 8 31% 4 22% 4 34% 48 42% 9 12%

Have installed and operate the technology on my site

7 35% 16 21% 5 17% 5 30% 3 18% 36 26% 55 67%

Not Answered 2 20% 3 9% 1 4% 1 16% 7 11% 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Describe your current level awareness and knowledge of the Energy Storage? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 1 1% Heard about it 5 8% 3 7% 3 21% 11 4% Know and understand the technology

2 1% 4 5% 9 33% 4 22% 4 37% 23 13% 9 11%


6 5% 11 11% 7 23% 7 52% 31 16% 13 16%


10 57% 19 70% 6 26% 2 11% 3 26% 40 46% 12 15%


13 37% 3 4% 16 15% 45 56%




Describe your current level awareness and knowledge of Fuel Cells. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 5 2% 5 1% 11 14% Heard about it 3 3% 3 11% 2 16% 8 4% 17 19% Know and understand the technology

6 4% 2 4% 7 29% 4 22% 4 37% 23 14% 8 11%


7 4% 1 2% 5 13% 6 44% 19 10% 7 8%


6 67% 10 59% 7 25% 1 4% 2 21% 26 46% 9 12%


30 34% 3 10% 3 19% 2 11% 38 13% 1 1%

Not Answered 4 20% 1 1% 4 11% 2 11% 1 16% 12 13% 28 34% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Describe your current level awareness and knowledge of IC Engines. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 8 5% 8 15% 2 4% 3 24% 21 7% 34 42% Heard about it 6 4% 5 4% 1 2% 2 21% 14 4% 6 8% Know and understand the technology

4 3% 3 5% 2 11% 2 13% 11 4% 5 6%


4 35% 6 49% 1 3% 3 33% 1 16% 15 30% 5 6%


3 32% 8 6% 9 34% 4 22% 1 5% 25 24% 2 2%


1 0% 11 18% 15 56% 7 33% 1 5% 35 21%




Describe your current level awareness and knowledge of Gas Turbines. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 5 4% 2 1% 2 6% 1 8% 10 3% 21 26% Heard about it 5 3% 6 12% 2 4% 2 11% 1 5% 16 6% 15 18% Know and understand the technology

6 4% 6 7% 5 18% 2 11% 2 24% 21 10% 11 14%


6 37% 9 53% 4 15% 5 41% 2 21% 26 35% 3 3%


4 32% 8 10% 8 34% 2 11% 3 18% 25 25% 2 2%


10 14% 4 12% 2 11% 1 8% 17 8%


Describe your current level awareness and knowledge of Mcroturbines. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 8 5% 4 6% 1 8% 13 4% 29 36% Heard about it 5 2% 9 14% 4 13% 3 29% 21 8% 15 18% Know and understand the technology

4 3% 4 3% 5 18% 3 33% 2 13% 18 10% 4 5%


6 37% 9 52% 4 15% 1 4% 2 24% 22 31% 2 2%


3 32% 8 9% 7 27% 2 11% 20 21% 2 2%


7 12% 6 19% 12 63% 25 14%




Describe your current level awareness and knowledge of Wind Turbines. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 3 1% 2 29% 5 4% 8 9% Heard about it 2 2% 2 1% 2 10% 1 4% 2 16% 9 4% 10 11% Know and understand the technology

6 4% 18 26% 12 40% 6 33% 3 32% 45 22% 21 26%


7 4% 5 6% 6 23% 4 15% 22 10% 6 7%


5 36% 15 60% 5 16% 1 7% 1 16% 27 33% 8 10%


2 5% 3 16% 5 2% 1 2%


Describe your current level awareness and knowledge of Pressure Reduction Turbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 12 8% 15 20% 10 41% 4 41% 4 26% 45 22% 39 48% Heard about it 9 5% 9 9% 5 14% 7 33% 30 11% 10 12% Know and understand the technology

3 3% 4 15% 1 4% 1 8% 9 5%


4 66% 3 48% 2 4% 1 4% 1 16% 11 39% 1 1%


1 0% 9 12% 2 5% 1 7% 2 11% 15 5% 1 1%


2 5% 3 12% 3 40% 8 6% 1 1%




Describe your current level awareness and knowledge of Waste Heat to Power. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 6 4% 4 3% 1 8% 11 3% 26 31% Heard about it 6 3% 7 12% 6 18% 1 4% 1 8% 21 9% 16 19% Know and understand the technology

4 2% 11 12% 3 16% 7 59% 3 18% 28 14% 6 8%


7 39% 5 49% 5 14% 2 7% 2 32% 21 32%


3 32% 8 10% 6 26% 2 11% 3 18% 22 23% 4 6%


7 11% 5 16% 1 4% 13 7% 1 2%


Priority Please rate the priority of the FIRST COST when selecting a technology.


Not at all Important

Slightly Important 1 1% 1 0% 1 1%

Moderately Important

3 1% 4 3% 2 8% 4 19% 1 8% 14 5% 14 17%

Quite Important 10 7% 10 16% 13 50% 4 19% 4 34% 41 21% 23 27%

Extremely Important 18 91% 29 80% 14 43% 8 63% 6 58% 75 73% 42 53%

Not Answered 1 1%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



Please rate the priority of the INCENTIVE LEVEL when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not at all Important 1 0% 1 0% 3 3%

Slightly Important 1 3% 1 4% 2 1% 3 4%


3 1% 6 5% 5 14% 3 11% 3 24% 20 7% 19 23%

Quite Important 14 40% 19 23% 14 58% 7 55% 3 26% 57 42% 34 43%


Not Answered 1 1%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Please rate the priority of the TAX BENEFITS when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not at all Important 2 1% 8 14% 5 15% 3 15% 5 37% 23 10% 7 8%

Slightly Important 3 3% 2 3% 1 3% 2 7% 1 8% 9 3% 8 10%


10 54% 12 14% 6 29% 4 37% 3 32% 35 36% 18 22%

Quite Important 9 6% 15 15% 11 35% 2 11% 37 15% 30 37%


Not Answered 2 1% 1 2% 1 4% 4 1% 2 2%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



Please rate the priority of the RELIABILITY RECORD when selecting a technology

nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important 1 0% 1 0%


3 2% 3 1% 3 3%

Quite Important 13 38% 8 9% 6 15% 4 19% 3 32% 34 24% 30 36%


Not Answered 1 0% 1 0% 3 3%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Please rate the priority of the OPERATION AND MAINTENANCE COST when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res



1 0% 1 2% 2 1% 3 3%

Quite Important 18 28% 21 31% 12 41% 9 41% 3 21% 63 33% 26 31%


Not Answered 1 1% 1 7% 2 1% 2 2%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



B.2 Capacity Weighted Survey Results Please rate your experience with the SGIP program - Overall Application Process

nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 1 0% 1 1% 1 3% 2 5% 5 2% 2 5%

Dissatisfied 4 1% 1 0% 2 3% 1 2% 8 1% 3 4%

Neutral 4 21% 7 12% 8 30% 1 3% 20 17% 10 16%

Satisfied 9 56% 20 31% 11 34% 10 82% 4 38% 54 40% 10 26%

Very Satisfied 5 17% 10 44% 5 21% 1 6% 3 49% 24 31% 8 13%

NA 8 5% 3 12% 1 9% 1 4% 1 10% 14 9% 22 36%

Not Answered 1 1%

Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%

Please rate your experience with the SGIP program - Incentive Amount nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Very Dissatisfied 1 1% 1 3% 1 5% 3 2% 1 3%

Dissatisfied 4 16% 2 2% 1 2% 1 0% 8 3% 2 3%

Neutral 3 0% 4 20% 6 31% 3 21% 1 2% 17 18% 8 20%

Satisfied 9 47% 23 36% 10 37% 9 69% 6 66% 57 46% 11 17%

Very Satisfied 7 32% 11 39% 9 27% 1 5% 1 21% 29 28% 10 18%

NA 7 2% 1 2% 1 10% 9 3% 19 31%

Not Answered 1 3% 1 0% 2 0% 5 8%

Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%



Please rate your experience with the SGIP program - Incentive Payout Time Frame nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Very Dissatisfied 3 0% 1 1% 2 3% 1 5% 7 2% 3 11%

Dissatisfied 5 44% 3 4% 1 9% 1 4% 1 2% 11 10% 4 4%

Neutral 5 6% 8 14% 6 19% 4 18% 2 49% 25 22% 11 21%

Satisfied 7 22% 20 28% 9 34% 8 68% 4 18% 48 30% 8 14%

Very Satisfied 2 16% 9 51% 7 28% 1 5% 1 21% 20 30% 7 11%

NA 7 7% 1 2% 1 4% 1 10% 10 5% 20 33%

Not Answered 2 4% 2 3% 4 2% 3 5%

Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%

Please rate your experience with the SGIP program - Program Requirements nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Very Dissatisfied 1 1% 2 3% 2 5% 5 2% 2 4%

Dissatisfied 4 44% 2 2% 3 16% 2 48% 1 2% 12 15% 3 13%

Neutral 6 6% 11 23% 8 25% 3 18% 1 24% 29 22% 13 22%

Satisfied 7 22% 19 30% 9 39% 8 29% 5 42% 48 35% 10 17%

Very Satisfied 5 17% 8 41% 4 15% 1 21% 18 22% 6 10%

NA 8 7% 1 2% 1 10% 10 3% 19 29%

Not Answered 1 3% 2 3% 3 2% 3 5%

Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%



Please rate the priority of the FIRST COST when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not at all Important

Slightly Important 1 2% 1 1% 1 1%

Moderately Important 3 15% 4 3% 2 15% 4 13% 1 24% 14 14% 14 14%

Quite Important 10 11% 10 26% 13 35% 4 6% 4 21% 41 25% 23 26%


Not Answered 1 1%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Please rate the priority of the INCENTIVE LEVEL when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not at all Important 1 0% 1 0% 3 3%



Quite Important 14 21% 19 36% 14 58% 7 59% 3 16% 57 41% 34 48%


Not Answered 1 1%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



Please rate the priority of the TAX BENEFITS when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not at all Important 2 0% 8 21% 5 12% 3 8% 5 30% 23 16% 7 7%

Slightly Important 3 15% 2 3% 1 1% 2 2% 1 24% 9 7% 8 9%


Quite Important 9 16% 15 24% 11 39% 2 7% 37 23% 30 36%


Not Answered 2 0% 1 2% 1 5% 4 1% 2 2%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Please rate the priority of the RELIABILITY RECORD when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not at all Important 1 0% 1 0%

Moderately Important 3 12% 3 1% 3 3%

Quite Important 13 33% 8 10% 6 17% 4 22% 3 51% 34 24% 30 38%


Not Answered 1 0% 1 0% 3 3%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Please rate the priority of the OPERATION AND MAINTENANCE COST when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res


Moderately Important 1 0% 1 3% 2 1% 3 3%

Quite Important 18 64% 21 41% 12 44% 9 37% 3 34% 63 43% 26 28%


Not Answered 1 1% 1 3% 2 0% 2 2%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



Please rate your level of satisfaction with each of the technologies that you installed nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not satified at all 1 15% 2 5% 3 4% 2 4% 8 5% 2 6%

Somewhat satified 2 0% 13 26% 6 27% 2 3% 1 7% 24 18% 10 9%

Satisfied 9 16% 11 17% 6 14% 6 32% 5 77% 37 28% 20 31%

Very satified 11 62% 17 49% 10 50% 5 59% 4 13% 47 44% 41 47%

NA 7 7% 1 3% 1 2% 9 2% 7 7%

Not Answered 1 0% 3 4% 1 2% 1 3% 6 2% 1 1%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Based on your experience, how likely are you to recommend the technology to your friends? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

Not at all 1 0% 13 29% 10 28% 4 11% 28 19% 6 9%

Very Likely 20 90% 28 61% 17 68% 12 89% 10 99% 87 76% 67 83%

NA 8 10% 1 3% 1 0% 10 2% 8 8%

Not Answered 2 0% 2 7% 1 3% 1 1% 6 3%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



Describe your current level awareness and knowledge of the Solar PV nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 2 2%

Heard about it 2 0% 2 0%

Know and understand the technology

2 0% 2 3% 5 17% 1 2% 2 27% 12 12% 4 4%


7 1% 5 13% 8 20% 5 54% 1 3% 26 15% 10 13%


11 53% 21 53% 8 29% 4 15% 4 53% 48 41% 9 18%


7 16% 16 31% 5 20% 5 26% 3 15% 36 22% 55 63%

Not Answered 2 29% 3 14% 1 4% 1 2% 7 9% 1 1%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Describe your current level awareness and knowledge of Energy Storage? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 1 1% Heard about it 5 8% 3 7% 3 32% 11 11% Know and understand the technology

2 0% 4 4% 9 26% 4 13% 4 37% 23 19% 9 15%


6 4% 11 19% 7 21% 7 62% 31 18% 13 15%


10 52% 19 61% 6 29% 2 4% 3 28% 40 38% 12 15%


13 43% 3 5% 16 6% 45 53%

Not Answered 2 3% 4 17% 3 21% 1 2% 10 9% 1 1%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%



Describe your current level awareness and knowledge of Fuel Cells

nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 5 1% 5 0% 11 12% Heard about it 3 1% 3 6% 2 25% 8 7% 17 16% Know and understand the technology

6 21% 2 6% 7 29% 4 13% 4 37% 23 22% 8 17%


7 16% 1 2% 5 18% 6 59% 19 13% 7 8%


6 32% 10 39% 7 29% 1 1% 2 21% 26 29% 9 15%


30 51% 3 11% 3 10% 2 14% 38 21% 1 1%


Describe your current level awareness and knowledge of IC Engines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 8 2% 8 10% 2 4% 3 49% 21 14% 34 43% Heard about it 6 10% 5 6% 1 0% 2 10% 14 5% 6 9% Know and understand the technology

4 12% 3 8% 2 7% 2 10% 11 6% 5 5%


4 31% 6 31% 1 3% 3 48% 1 14% 15 19% 5 6%


3 16% 8 14% 9 26% 4 11% 1 7% 25 17% 2 2%


1 0% 11 26% 15 64% 7 35% 1 7% 35 34%




Describe your current level awareness and knowledge of Gas Turbines


No Idea 5 1% 2 1% 2 4% 1 1% 10 2% 21 28% Heard about it 5 1% 6 9% 2 3% 2 8% 1 7% 16 5% 15 18% Know and understand the technology

6 21% 6 7% 5 18% 2 7% 2 7% 21 13% 11 14%


6 32% 9 45% 4 18% 5 53% 2 21% 26 30% 3 4%


4 16% 8 13% 8 35% 2 4% 3 39% 25 26% 2 2%


10 20% 4 15% 2 8% 1 24% 17 16%


Describe your current level awareness and knowledge of Mcroturbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 8 2% 4 6% 1 25% 13 6% 29 35% Heard about it 5 12% 9 11% 4 8% 3 34% 21 14% 15 21% Know and understand the technology

4 9% 4 3% 5 18% 3 50% 2 10% 18 14% 4 5%


6 32% 9 39% 4 9% 1 0% 2 15% 22 20% 2 2%


3 16% 8 18% 7 32% 2 14% 20 21% 2 2%


7 17% 6 25% 12 50% 25 17%




Describe your current level awareness and knowledge of the Wind Turbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 3 0% 2 48% 5 3% 8 8%

Heard about it 2 1% 2 2% 2 12% 1 4% 2 25% 9 10% 10 10%


6 10% 18 26% 12 45% 6 21% 3 30% 45 32% 21 25%


7 16% 5 15% 6 18% 4 8% 22 13% 6 6%


5 34% 15 47% 5 17% 1 3% 1 14% 27 25% 8 11%


2 7% 3 21% 5 6% 1 7%

Not Answered 8 40% 2 3% 4 8% 2 17% 2 9% 18 11% 27 33%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Describe your current level awareness and knowledge of Pressure Reduction Turbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 12 17% 15 17% 10 27% 4 55% 4 63% 45 32% 39 46% Heard about it 9 7% 9 16% 5 23% 7 18% 30 15% 10 16% Know and understand the technology

3 3% 4 16% 1 2% 1 3% 9 8%


4 41% 3 32% 2 5% 1 5% 1 14% 11 18% 1 1%


1 6% 9 18% 2 6% 1 3% 2 14% 15 11% 1 1%


2 9% 3 14% 3 6% 8 9% 1 1%




Describe your current level awareness and knowledge of the Waste Heat to Power nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No Idea 6 1% 4 4% 1 1% 11 1% 26 26%

Heard about it 6 1% 7 10% 6 11% 1 1% 1 25% 21 11% 16 18%


4 17% 11 13% 3 19% 7 68% 3 17% 28 20% 6 11%


7 35% 5 34% 5 24% 2 4% 2 17% 21 25%


3 16% 8 19% 6 21% 2 4% 3 38% 22 22% 4 7%


7 18% 5 16% 1 2% 13 11% 1 7%

Not Answered 5 30% 2 3% 4 9% 3 22% 1 2% 15 9% 28 31%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

Would you have installed the technology in teh absence of SGIP funding nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res

No, not at all 20 50% 25 43% 13 57% 5 18% 3 6% 66 40% 21 19%

Maybe, at a later time 9 47% 14 39% 9 22% 10 81% 5 57% 47 40% 23 37%

Yes, definitely 2 3% 3 10% 6 18% 1 24% 12 14% 10 17%

Not Answered 2 7% 1 2% 1 1% 2 14% 6 5% 27 27%

Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%

POLICIES IMPACTING DG AND STORAGE | C-1

POLICIES IMPACTING DG AND STORAGE

Table C-1 is a listing of programs or policies identified by our research as having possible impacts on DG or storage development in California.

Table C-1: Listing of Programs or Policies Impacting DG and Storage in California

Programs or Policies Programs or Policies Marin County - Green Building Requirements Renewable Market Adjusting Tariff (ReMAT) Palo Alto Utilities - Remote Renewables Program County of San Bernardino - Green Building Incentive Small Business Energy Loan Program City of San Francisco - Green Building Requirement for

City Buildings Energy Technology Export Program City of San Francisco - Green Building Code Energy Innovations Small Grant (EISG) Program Sonoma County - Energy Independence Program City of San Diego - Green Power Purchasing Anaheim Public Utilities - Low-Interest Energy

Efficiency Loan Program City of San Diego - Sustainable Building Policy City of Palo Alto - Green Building Requirement City of Santa Monica - Green Building Grant Program County Wind Ordinance Standards Small Wind Access Law SMUD - Feed-in Tariff City of San Francisco - Renewable Energy Purchasing Sales and Use Tax Exclusion for Advanced

Transportation and Alternative Energy Manufacturing Program

Net Metering Marin Clean Energy - Feed-In Tariff City of Santa Monica - Green Power Purchasing City of Los Angeles - Green Building Retrofit

Requirement Public Benefits Funds for Renewables and Efficiency LADWP - Net Metering City of San Jose - Municipal Green Building Program Los Angeles County - Green Building Program Emerging Renewables Program Los Angeles County - LEED for County Buildings Self-Generation Incentive Program San Diego County - Wind Regulations Solar Energy and Distributed Generation Grant Program

San Diego County - Design Standards for County Facilities

Solar or Wind Energy System Credit - Corporate Orange County - Small Wind Energy Systems Solar or Wind Energy System Credit - Personal Santa Clara County - Zoning Ordinance Energy Financing Industrial Development Bond Program

Renewable Auction Mechanism (RAM)

Interconnection Standards Santa Clara County - Green Building Policy for County Government Buildings

Public Leadership Solutions for Energy (PULSE) Loan Energy Efficiency Financing for Public Sector Projects Renewables Portfolio Standard Los Angeles County - Commercial PACE Agricultural Biomass to Energy Program Western Riverside Council of Governments - Home

Energy Renovation Opportunity (HERO) Financing



Programs or Policies Programs or Policies Program

Supplemental Energy Payments (SEPs) Western Riverside Council of Governments - Large Commercial PACE

City of Oakland - Self Certification for Renewables CaliforniaFIRST Energy Efficiency Financing Program LADWP - Feed-in Tariff (FiT) Program Burbank Water & Power - Green Building Incentive Program

California Enterprise Development Authority (Figtree PACE) - Statewide PACE Program

Green Building Action Plan for State Facilities Feed-in-Tariff City of Berkeley - Green Building Standards for City Owned and Operated Projects

California Natural Gas Rates

City of Santa Monica - Expedited Permitting for Green Buildings

Palm Desert - Energy Independence Program

SCE - Biomass Standard Contract City of San Francisco - PACE Agriculture and Food Processing Energy Loans Emissions Performance Standard City of Anaheim - Green Building Program Climate Change Scoping Plan City of Palo Alto Utilities - Renewable Energy Credit Purchase Program

Certain of the programs and policies were considered to have more influence on DG and storage development in California and were designated as “primary” programs or policies. Table C-2 is a listing of the primary programs and policies along with a summary.

Table C-2: Summary of Primary Programs or Policies Impacting DG and Storage in California

Primary Programs or Policies Summary of Program or Policy Energy Innovations Small Grant (EISG) Program

The Energy Innovations Small Grant (EISG) Program is administered by the California Energy Commission. The EISG provides up to $150,000 for hardware projects and $75,000 for modeling projects to small businesses, non-profits, individuals and academic institutions to conduct research that establishes the feasibility of new, innovative energy concepts. Research projects must target one of the PIER R&D areas, address a California energy problem and provide a potential benefit to California electric and natural gas ratepayers.

Small Wind Access Law Solar and wind access laws are designed to establish a right to install and operate a solar or wind energy system at a home or other facility. Some solar access laws also ensure a system owner’s access to sunlight. AB 1207 (2001) in California established certain rights for small wind applications.

Net Metering California's net-metering law originally took effect in 1996 and applies to all utilities with one exception (LADWP due to size). The law has been amended numerous times since its enactment, most recently by AB 327 of 2013. The original law applied to wind-energy systems, solar-electric systems and hybrid (wind/solar) systems. In September 2002, legislation (AB 2228) allowed biogas-electric facilities up to 1 megawatt (MW) to net meter until December 31, 2005, under a pilot program. This pilot program was extended until December 31, 2009, upon the



Primary Programs or Policies Summary of Program or Policy

enactment of AB 728 in September 2005. SB 489 did away with the pilot program, and instead allowed for biomass and all other RPS-eligible technologies to participate in net metering under the same terms available for solar and wind. Other legislation enacted in October 2003 (AB 1214) made fuel cells eligible for net metering until the cumulative rated generating capacity of net-metered fuel cells reaches 112.5 MW statewide. AB 2165 increased the statewide maximum to 500 MW, and requires each utility to provide net metering for eligible fuel cells until it reaches its proportionate share of the 500 MW cap. Previously restricted to fuel cells that begin operation prior to January 1, 2014, AB 2165 of 2012 extended the eligibility deadline to January 1, 2015, and AB 327 of 2013 further extended the deadline to January 1, 2017

Public Benefits Funds for Renewables and Efficiency

California's 1996 electric industry restructuring legislation (AB 1890) directed the state’s three major investor-owned utilities (Southern California Edison, Pacific Gas and Electric Company, and San Diego Gas & Electric) to collect a "public goods charge" (PGC) on ratepayer electricity use from 1998 through 2001 to create public benefits funds for renewable energy, energy efficiency, and research, development & demonstration (RD&D). Subsequent legislation in 2000 extended the programs for 10 years from 2002 to 2012. This fund is used by the California Energy Commission (CEC) to administer renewable energy and RD&D programs, and by the electric utilities to administer energy efficiency incentive programs. The California Public Utilities Commission separately collects funds to administer the California Solar Initiative, the Self-Generation Incentive Program, the Renewables Portfolio Standard and others.

Emerging Renewables Program

Assembly Bill 1890 (Brulte, Chapter 854, Statutes of 1996) and Senate Bill 90 (Sher, Chapter 905, Statutes of 1997) created the Energy Commission's Renewable Energy Program. The Legislature directed that a portion of the funds collected from the customers of the three major investor-owned utilities (IOUs) be used for statewide public benefit programs, including incentives for renewable electricity systems. Under this legislation, the program continued through 2002 to provide financial incentives to support existing, new, and emerging renewable resources in a market environment. In 2002, Senate Bill 1038 (Sher, Chapter 515, Statutes of 2002), reauthorized Renewable Energy Program through 2006. Its approach, however, for supporting renewable energy development was impacted by the energy crisis of 2000 and 2001. In accordance with the changes to law under Senate Bill 1018, the Energy Commission took taking immediate steps to close out the Emerging Renewables Program (ERP). Technologies that were eligible for the ERP became eligible for the SGIP when the ERP ended.

Self-Generation Incentive Program

The CPUC's Self-Generation Incentive Program (SGIP) provides incentives to support existing, new, and emerging distributed energy resources. The SGIP provides rebates for qualifying distributed energy systems installed on the customer's side of the utility meter. Qualifying technologies include wind turbines, waste heat to power technologies, pressure reduction turbines, internal combustion engines, microturbines, gas turbines, fuel cells, and advanced energy storage systems. The Self Generation Incentive Program (SGIP) - with 544 completed projects for a total capacity of 252 megawatts - is one of the longest-running and most successful distributed generation incentive programs in the country. In 2011 alone, these facilities provided over 760,000 MWh of




electricity to the California, enough electricity to meet the needs of over 116,000 homes. The program continues to make strides toward a cleaner, distributed-energy future.

Solar Energy and Distributed Generation Grant Program

In 2000, Senate Bill 1345 (Statutes of 2000, Chapter 537, Peace) directed the Energy Commission to develop and administer a grant program to support the purchase and installation of solar energy and selected small distributed generation systems. Solar energy systems include solar energy conversion to produce hot water, swimming pool heating, and battery backup for photovoltaic (PV) applications. Funding for the program had to be renewed annually by the Legislature. The state's budget crisis essentially ended the program

Solar or Wind Energy System Credit - Corporate

The federal business energy investment tax credit available under 26 USC § 48 was expanded significantly by the Energy Improvement and Extension Act of 2008 (H.R. 1424), enacted in October 2008. This law extended the duration -- by eight years -- of the existing credits for solar energy, fuel cells and microturbines; increased the credit amount for fuel cells; established new credits for small wind-energy systems, geothermal heat pumps, and combined heat and power (CHP) systems; allowed utilities to use the credits; and allowed taxpayers to take the credit against the alternative minimum tax (AMT), subject to certain limitations. The credit was further expanded by the American Recovery and Reinvestment Act of 2009, enacted in February 2009. (30% for solar, fuel cells, small wind)

Solar or Wind Energy System Credit - Personal

Established by The Energy Policy Act of 2005, the federal tax credit for residential energy property initially applied to solar-electric systems, solar water heating systems and fuel cells. The Energy Improvement and Extension Act of 2008 extended the tax credit to small wind-energy systems and geothermal heat pumps, effective January 1, 2008. Other key revisions included an eight-year extension of the credit to December 31, 2016; the ability to take the credit against the alternative minimum tax; and the removal of the $2,000 credit limit for solar-electric systems beginning in 2009. The credit was further enhanced in February 2009 by The American Recovery and Reinvestment Act of 2009, which removed the maximum credit amount for all eligible technologies (except fuel cells) placed in service after 2008. A taxpayer may claim a credit of 30% of qualified expenditures for a system that serves a dwelling unit located in the United States that is owned and used as a residence by the taxpayer. Expenditures with respect to the equipment are treated as made when the installation is completed. If the installation is at a new home, the "placed in service" date is the date of occupancy by the homeowner. Expenditures include labor costs for on-site preparation, assembly or original system installation, and for piping or wiring to interconnect a system to the home. If the federal tax credit exceeds tax liability, the excess amount may be carried forward to the succeeding taxable year. The excess credit may be carried forward until 2016, but it is unclear whether the unused tax credit can be carried forward after then. The maximum allowable credit, equipment requirements and other details vary by technology. 30%

Energy Financing Industrial Development Bond Program

Industrial Development Bonds (IDBs) are tax-exempt securities issued up to $10 million by a governmental entity to provide money for the acquisition, construction, rehabilitation and equipping of manufacturing and processing facilities for private companies. IDBs can be issued by the I-Bank, local Industrial Development Authorities, or by Joint Powers Authorities. IDBs are relevant to clean




energy development in two major ways. First, manufacturers in the supply chain of clean energy (e.g., solar panel, wind turbine and biomass equipment manufacturers) can receive tax-exempt financing. States or regions looking to cultivate a clean energy manufacturing cluster can look at (a) targeting IDB marketing to such companies and (b) creating an enhancement or similar program to facilitate the use of IDBs by clean energy manufacturers. Second, changes to the federal tax code for IDBs could allow any manufacturer to place clean energy generation equipment on their facilities. Whole-facility clean energy generation is not a currently permitted use of IDB proceeds, but was temporarily allowed from 2009-2010. Congress could act to authorize a permanent rule change, making tax-exempt bonds an option for all small- to mid-sized American manufacturers seeking lower-cost, clean energy.

Interconnection Standards

California's "Rule 21" generally applies to systems connecting to an investor-owned utility’s distribution grid, non-export generating facilities connecting to an investor-owned utility’s transmission grid and all net metered facilities in an investor-owned utility’s service territory. Systems connecting to an investor-owned utility’s distribution grid for the purpose of participating in a wholesale transaction must apply under the investor-owned utility’s Wholesale Distribution Access Tariff. Systems connecting to the transmission grid must apply to the California Independent System Operator for interconnection. Systems connecting to the grid of a municipal or cooperative utility must follow the interconnection procedures adopted by that utility. Rule 21 clearly defines a series of screens meant to filter applicants into the study path most suited for their project. It also establishes fixed timelines for the screens intended to speed the process of approval. Also defined in the tariff are a variety of fees and deposits required at various stages of the interconnection process. Net metered facilities are exempt from most of these fees

Public Leadership Solutions for Energy (PULSE) Loan

The California Consumer Power and Conservation Financing Authority (the Power Authority or CPA) has created the Public Leadership Solutions for Energy (PULSE) fund to provide loans to help finance energy efficiency and renewable energy projects in public buildings. The program is available to cities, counties, special districts, schools and community colleges across California, as well as State and other local agencies. This loan pool overcomes limitations of other State energy loan programs by supporting larger transactions, a broader range of eligible technologies, and longer loan terms. Loan amounts of $2 million or more with no maximum are available for up to the expected life of the project. Tax-exempt market rates: for short-term or variable rate loans are currently as low as 3%; low longer-term rates range from 4.5-5% up to projects’ useful life. Fixed or variable rate debt options are available and bonds issued under the program will be insured. Agencies still can claim rebates, buydowns, and grants from other sources. Eligible projects include Energy efficiency. Advanced building metering and controls. Thermal storage. On-site renewable energy (solar PV, small scale wind, biogas and landfill gas recovery). On-site distributed generation (fuel cells, micro-turbines, combined heat and power). Incremental costs of exceeding Title 24 building energy standards in new construction and major renovations (for green or sustainable buildings). Financeable costs include feasibility and engineering design, performance guarantees, equipment warranties, project management (managing bidding, equipment procurement, construction management, and commissioning




the final outcome). Renewables Portfolio Standard

California’s Renewables Portfolio Standard (RPS) was originally established by legislation enacted in 2002. Subsequent amendments to the law have resulted in a requirement for California’s electric utilities to have 33% of their retail sales derived from eligible renewable energy resources in 2020 and all subsequent years. The law established interim targets for the utilities. Publicly Owned Municipal Utilities (POUs) are not regulated by the CPUC but are affected by the law nonetheless, and their governing boards are charged with establishing procurement requirements. AB 327 (2013) allows the CPUC to establish procurement requirements in excess of the percentages.

Agricultural Biomass to Energy Program

The California Energy Commission (CEC) initiated the Agricultural Biomass-to-Energy Program following the enactment of state Senate Bill 704 (SB 704) in September 2003. SB 704 directed the CEC to design and implement a program to help improve California’s air quality in 1 agricultural communities by reducing open-field burning of agricultural residues. Through this program, the CEC offered grants of $10 per ton of biomass to eligible facilities converting biomass to electricity from July 1, 2003 to June 30, 2004. To be eligible for support under this solicitation, facilities were required to increase their purchase of qualified agricultural biomass by at least 10% above their five-year average of qualified purchases. The CEC allocated $6 million for this one-year program, and provided payments upon demonstration that these requirements had been met. However, the $6 million was entirely allocated to nine facilities for purchases occurring by February 2004, and purchases made later in the year will not receive support. There are currently no plans for a continuation of this program.

Dairy Power Production Program

"Signed into law on April 11, 2001, Senate Bill 5X created the Dairy Power Production Program and authorized the CEC to expend $9.64 million (roughly $8.6 million of which is available for project awards, with the rest covering administrative expenses) to encourage the development of anaerobic digestion and gasification (“biogas”) electricity generation projects on California dairies. The CEC in turn signed a contract with the Western United Resource Development Corporation (WURD) to administer the program. WURD is a non-profit entity created to administer the CEC program and is associated with the Western United Dairymen, a trade association of dairy farmers and producers in California. The original goal of the program was to install over five megawatts of dairy biogas systems capable of generating over 30 million kWh annually by September 30, 2002 (the CEC subsequently extended the timeline in recognition of several barriers discussed below). The CEC has estimated that approximately 100 MW of near term biogas production potential from livestock manure exists in the state, with only 370 kW in place today. As of May 2004, roughly 50 projects had applied to the program, and a total of $5,792,370 had been set aside to fund 14 projects with a total estimated generating capacity of 3.5 MW.

SCE - Biomass Standard Contract

SCE's Biomass Standard Contract process was made available in 2007 offering three contact vehicles based on capacity (100 kW to 1 MW; 1 - 5 MW; and greater than 5 MW). The contract provided a faster, simpler way for biomass projects under 20 MW to see power to utility customers. SCE’s new contracts were to be available until Dec. 31, 2007, but could be withdrawn after a total of 250 MW was enrolled.

SMUD - Feed-in Tariff Under a Feed-In Tariff (FIT), utilities such as SMUD offer standard published rates




and contract terms for the purchase of electricity made from renewable energy resources and combined heat-power installations. SMUD's Feed-In Tariff is for eligible renewable energy resources and qualifying combined heat and power installations up to 5 MW. Effective August 4, 2010, SMUD suspended the acceptance of new feed-in-tariff applications.

LADWP - Net Metering LADWP allows its customers to net meter their photovoltaic (PV), wind, and hybrid systems with a capacity of not more than one megawatt. LADWP will provide the necessary metering equipment unless an installation requires atypical metering equipment. In these cases the customer must cover the additional metering expenses. The customer must also pay any related interconnection fees. Excess kilowatt-hours (kWh) generated by the customer's system will be credited toward their future bills. Excess bill credits, however, may not be used to offset taxes, minimum charges, or other charges which are not based on energy. If a bill credit still remains when the customer terminates service, the balance will be granted to the utility.

San Diego County - Wind Regulations

The County of San Diego has established zoning guidelines for wind turbine systems of varying sizes in the unincorporated areas of San Diego County. Wind turbine systems can be classified as small, medium, or large, and have different siting requirements. Turbines of all sizes must abide by Noise Abatement and Control laws, must have restricted public access using locked fences, non-climbable towers, or other restrictions, and must have appropriate warning signs posted. A wind turbine is considered non-operational if it produces less than 10% of the expected power output for 12 consecutive months. Small Wind Turbine System: An installation consisting of no more than one wind turbine and a blade sweep area of 220 square feet or less. Small systems must be installed on a parcel of at least 1 acre and should be set back from property lines and roads at least 2 times the height of the wind system. The height of the turbine may not exceed 60 feet. Specific zone and fire setback requirements also apply. Non-operational turbines must be removed within 12 months after becoming non-operational. Medium Wind Turbine System: Installation consisting of one to five turbines with a combined blade sweep area of 850 square feet or less. A medium system must be installed on a parcel of at least 1 acre and requires an Administrative Permit. Turbines must not exceed 60 feet in height and must be set back from property lines and roads at least three times the height of the tower. Specific zone and fire setback requirements also apply. Non-operational turbines must be removed within 12 months after becoming non-operational.

Orange County - Small Wind Energy Systems

In December 2010, the County of Orange Board of Supervisors adopted small wind performance and development standards (Ord. No. 10-020) in order to promote distributed generation systems in non-urbanized areas (as defined in Government Code Section 65944(d)(2)) within the unincorporated territory. Permitting standards are for systems of 50 kW or less per customer site, for which the energy is primarily for on-site consumption. Height: For systems 45 feet tall or less, a use permit must be approved by the Zoning Administrator, and for systems between 45 and 80 feet in height, the use permit must be approved by the Planning Commission. Systems of up to 100 feet may be considered if special circumstances can be demonstrated. Number of Units: For systems of 60 feet or less, there may not be more than 2 systems on a lot of 5 acres or less. For every additional 5 acres




of land, 1 more system may be added, for up to 5 total systems. For systems taller than 60 feet, only 1 system is allowed per 10 acres of land. No more than 3 systems total are allowed.

Western Riverside Council of Governments - Large Commercial PACE

The Western Riverside Council of Governments (WRCOG) Large Commercial PACE Financing Program encourages Residential customers to borrow money for the following energy efficiency projects: Wind, Geothermal Heat Pumps, Solar Water Heat, Photovoltaic, Caulking/Weather-stripping, Duct/Air sealing, Building Insulation, Windows, Roofs, Custom/Others pending approval, Whole House Fans, Pool Pumps, Evaporative Coolers, Water Efficiency. Lighting, Furnaces, Boilers, Heat pumps, Central Air conditioners, and Programmable Thermostats. The customer can receive from $5k min to $1million be repaid in 25 years.

Emissions Performance Standard

SB 1368 (Perata, Chapter 598, Statutes of 2006) limits long-term investments in baseload generation by the state's utilities to power plants that meet an emissions performance standard (EPS) jointly established by the California Energy Commission and the California Public Utilities Commission. Among the requirements are the following: Establish a standard for baseload generation owned by, or under long-term contract to publicly owned utilities, of 1,100 lbs CO2 per megawatt-hour (MWh). This will encourage the development of power plants that meet California's growing energy needs while minimizing their emissions of greenhouse gases; Require posting of notices of public deliberations by publicly owned utilities on long-term investments on the Energy Commission website. This will facilitate public awareness of utility efforts to meet customer needs for energy over the long-term while meeting the State's standards for environmental impact, and Establish a public process for determining the compliance of proposed investments with the EPS.

Climate Change Scoping Plan

Assembly Bill 32 (AB 32) required the California Air Resources Board (ARB or Board) to develop a Scoping Plan that describes the approach California will take to reduce greenhouse gases (GHG) to achieve the goal of reducing emissions to 1990 levels by 2020. The Scoping Plan was first considered by the Board in 2008 and must be updated every five years. The Board approved the First Update to the Climate Change Scoping Plan on May 22, 2014. In the Update, nine key focus areas were identified (energy, transportation, agriculture, water, waste management, and natural and working lands), along with short-lived climate pollutants, green buildings, and the cap-and-trade program.



Table C-3 is a listing of policies or legislation specific to the SGIP and a summary.

Table C-3: Policies Specifically Related to SGIP

Policies Specifically Related to

SGIP Summary of Policies or Legislation AB 970 Assembly Bill required the CPUC to initiate load control and distributed generation activities D. 01-03-073 CPUC Decision complying with Assembly Bill 970 and establishing the Self Generation Incentive

Program. Implementation of PU Code Section 399.15(b), Paragraph 4-7; Load Control and Distributed Generation Initiatives

D. 01-06-035 CPUC Decision establishing waste heat recovery standards for SGIP. Requires Energy Branch to develop reliability criteria.

D. 02-02-26 CPUC Decision addressing eligibility of customers served by electric municipalities, maximum size and annual program budget.

D. 02-04-004 CPUC Decision clarifying Applicant’s ability to receive incentive funding from multiple sources. Addressing SCAQMD’s PTM of Decision 01-03-073

D. 02-09-051 CPUC Decision adding technology level 3-R, which establishes a new level of incentives. Contains specific requirements for projects using renewable fuels for level 3-R. Addressing Capstone’s PTM

AB 1684 Extended the SGIP through 2007; Required that projects commencing January 1, 2005 meet a NOx emission standard; Required that projects commencing January 1, 2007 meet a more stringent NOx emission standard and a minimum system efficiency standard; Established a NOx emission credit that can be used by combined heat and power (CHP) units to meet minimum system efficiency standard

D. 04-12-045 Modified SGIP to incorporate provisions of AB 1685: Eliminates maximum percentage payment limits; Reduces incentive payments for several technologies; Expands opportunities for public input regarding developing a declining incentive schedule, developing an exit strategy and adopting a data release format; Required an application fee for all projects received after 1/1/2005 in order to deter against “phantom projects”. This requirement was removed beginning in 2007 except in the case of new technologies that are in the process of certification.

D. 06-01-047 Established the California Solar Initiative (CSI) and ordered changes in the 2006 SGIP to accommodate the transition of solar program elements to the CSI beginning January 1, 2007.

AB 2778 Extended SGIP until January 1, 2012; Limited eligible technologies beginning January 1, 2008 to fuel cells and wind systems that meet emissions standards required under the distributed generation certification program adopted by the State Air Resources Board; Requires that eligibility of non-renewable fuel cell projects be determined either by calculating electrical and process heat efficiency according to PU Code 216.6 or by calculating overall electrical efficiency

D. 08-04-049 Removed the 1 MW cap on incentives for 2008 and 2009 allowing projects to receive lower incentives on a tiered structure for the portion of a system over 1 MW.

AB 2267 Requires an additional 20% incentive for the installation of eligible distributed generation resources from a California Supplier. This additional incentive is applied only to the technology portion of the incentive; the additional incentive for renewable fuels is not included in calculating the 20%.

D. 08-11-044 Determined that Advanced Energy Storage systems coupled with eligible SGIP technologies will receive an incentive of $2/watt of installed capacity; Revises the process for the review of SGIP



Policies Specifically Related to

SGIP Summary of Policies or Legislation program modification requests

D. 09-09-048 Grants a petition to modify SGIP policies expanding eligibility for Level 2 incentives to include “directed biogas” projects where renewable fuel is nominated via contract

D. 10-02-017 Revises Decision 08-11-044 so that Advanced Energy Storage systems coupled with fuel cells must meet the site specific requirements for on-site peak demand reduction and be capable of discharging fully at least once per day in order to be eligible for the$2/watt incentive from the self-generation incentive program; Determines that Advanced Energy Storage systems coupled with eligible technologies under the SGIP must install metering equipment capable of measuring and recording interval data on generation output and Advanced Energy Storage system charging and discharging

D. 11-09-015 Adds eligibility requirements based upon greenhouse gas reductions; Establishes an on-site emission rate that projects must beat to be eligible for SGIP participation of 379 kg CO2/MWh; Adds Waste Heat to Power, Pressure Reduction Turbine, Internal Combustion Engine – CHP, Microturbine – CHP, Gas Turbine – CHP, Stand-Alone AES technologies to the list of eligible technologies; Revises the incentive levels for all technologies and adds a $2.00/Watt biogas adder; Directs that Directed Biogas can only be procured from in-state suppliers; Eliminates maximum size restrictions given a project meets on-site load. Sets a 30 kW minimum for wind and renewable fueled fuel cell projects; Adopts a hybrid payment structure with 50% upfront, 50% PBI based on kWh generation of on-site load for projects 30 kW and larger. Projects under 30 kW will receive the entire incentive upfront;

D. 11-09-015 (cont'd)

Adopts the following assumed capacity factors to be used in PBI calculations: 10% for AES, 25% for wind, and 80% for all other distributed energy resources; Implements incentive decline in the following manner 10% per year for emerging technologies and 5% per year for all other technologies, beginning 1/1/2013; Adopts a supplier concentration limit where no more than 40% of the annual statewide budget available on the first of a given year may be allocated to any single manufacturer’s technology during that year; Establishes a maximum project incentive of $5 million; Establishes that the minimum customer investment in a project must be 40% of eligible project costs; Establishes an SGIP incentive budget allocation of 75% for renewable and emerging technologies, and 25% for non-renewable technologies; Determines that the Program Administration Budget will be reduced to 7%; Establishes that projects exporting to the grid are eligible for SGIP incentives as long as they do not export more than 25% on an annual net basis; Makes an energy efficiency audit mandatory for participation in SGIP unless an extensive audit has been conducted within five years of the date of the reservation request; Establishes an application fee that is 1% of the amount of incentive requested; Limits all projects to one six month extension. Request for a second extension may be made to the Working Group; Extends the warranty period to 10 years.

D. 12-05-037 Orders that all technologies previously eligible for the Emerging Renewables Program should be immediately eligible for the SGIP; Determines that consolidating the ERP and SGIP programs now is preferable to perpetuating two competing programs that serve the same types of technologies and policy purposes.

SITEPRO BUILDING TYPE TO NAICS CODE MAPPING | D-1

APPENDIX D SITEPRO BUILDING TYPE TO NAICS CODE MAPPING

We mapped 2012 Census data NAICS codes using from 2-digit to 6-digit width to corresponding SitePro building type. Table D-1 lists the NAICS codes, establishment descriptions, and assigned SitePro building type. Many commercial and some industrial NAICS codes were assigned with SitePro’s large or small office building types. We differentiate large versus small office based on employee count bins in Census data.

Table D-1: Commercial and Industrial NAICS Codes and Associated SitePro Building Type

NAICS Code NAICS Description SitePro Building Type

6112 Junior Colleges College

6113 Colleges, Universities, and Professional Schools College

311 Food Manufacturing Food Manufacturing

312 Beverage and Tobacco Product Manufacturing Food Manufacturing

445 Food and Beverage Stores Food Store

622 Hospitals Health, Hospital

623 Nursing and Residential Care Facilities Health, Hospital

721 Accommodation Lodging, Hotel

71391 Golf Courses and Country Clubs Lodging, Hotel

71394 Fitness and Recreational Sports Centers Lodging, Hotel

92214 Correctional institutions Lodging, Hotel

53111 Lessors of Residential Buildings and Dwellings MultiFamily

23 Construction Office (large/small)

52 Finance and Insurance Office (large/small)

54 Professional, Scientific, and Technical Services Office (large/small)

55 Management of Companies and Enterprises Office (large/small)

488 Support Activities for Transportation Office (large/small)

511 Publishing Industries (except Internet) Office (large/small)

512 Motion Picture and Sound Recording Industries Office (large/small)

515 Broadcasting (except Internet) Office (large/small)

517 Telecommunications Office (large/small)

518 Data Processing, Hosting, and Related Services Office (large/small)

519 Other Information Services Office (large/small)

561 Administrative and Support Services Office (large/small)

621 Ambulatory Health Care Services Office (large/small)

624 Social Assistance Office (large/small)

921 Executive, legislative and general government Office (large/small)




923 Administration of human resource programs Office (large/small)

924 Administration of environmental programs Office (large/small)

925 Community and housing program administration Office (large/small)

926 Administration of economic programs Office (large/small)

927 Space research and technology Office (large/small)

928 National security and international affairs Office (large/small)

42511 Business to Business Electronic Markets Office (large/small)

42512 Wholesale Trade Agents and Brokers Office (large/small)

53119 Lessors of Other Real Estate Property Office (large/small)

53121 Office (large/small)s of Real Estate Agents and Brokers Office (large/small)

53131 Real Estate Property Managers Office (large/small)

53132 Office (large/small)s of Real Estate Appraisers Office (large/small)

53139 Other Activities Related to Real Estate Office (large/small)

53241 Construction, Transportation, Mining, and Forestry Machinery and Equipment Rental and Leasing Office (large/small)

53242 Office (large/small) Machinery and Equipment Rental and Leasing Office (large/small)

53249 Other Commercial and Industrial Machinery and Equipment Rental and Leasing Office (large/small)

53311 Lessors of Nonfinancial Intangible Assets (except Copyrighted Works) Office (large/small)

71131 Promoters of Performing Arts, Sports, and Similar Events with Facilities Office (large/small)

71132 Promoters of Performing Arts, Sports, and Similar Events without Facilities Office (large/small)

71141 Agents and Managers for Artists, Athletes, Entertainers, and Other Public Figures Office (large/small)

71151 Independent Artists, Writers, and Performers Office (large/small)

71211 Museums Office (large/small)

71212 Historical Sites Office (large/small)

71329 Other Gambling Industries Office (large/small)

92211 Courts Office (large/small)

92212 Police protection Office (large/small)

92213 Legal counsel and prosecution Office (large/small)

811211 Consumer Electronics Repair and Maintenance Office (large/small)

811212 Computer and Office (large/small) Machine Repair and Maintenance Office (large/small)

811213 Communication Equipment Repair and Maintenance Office (large/small)

811219 Other Electronic and Precision Equipment Repair and Maintenance Office (large/small)

811412 Appliance Repair and Maintenance Office (large/small)

811420 Reupholstery and Furniture Repair Office (large/small)

811430 Footwear and Leather Goods Repair Office (large/small)

811490 Other Personal and Household Goods Repair and Maintenance Office (large/small)




812921 Photofinishing Laboratories (except One-Hour) Office (large/small)

812922 One-Hour Photofinishing Office (large/small)

812990 All Other Personal Services Office (large/small)

813110 Religious Organizations Office (large/small)

813211 Grantmaking Foundations Office (large/small)

813212 Voluntary Health Organizations Office (large/small)

813219 Other Grantmaking and Giving Services Office (large/small)

813311 Human Rights Organizations Office (large/small)

813312 Environment, Conservation and Wildlife Organizations Office (large/small)

813319 Other Social Advocacy Organizations Office (large/small)

813410 Civic and Social Organizations Office (large/small)

813910 Business Associations Office (large/small)

813920 Professional Organizations Office (large/small)

813930 Labor Unions and Similar Labor Organizations Office (large/small)

813940 Political Organizations Office (large/small)

813990 Other Similar Organizations (except Business, Professional, Labor, and Political Organizations) Office (large/small)

722 Food Services and Drinking Places Restaurant, Sit-Down

71111 Theater Companies and Dinner Theaters Restaurant, Sit-Down

441 Motor Vehicle and Parts Dealers Retail, Large

442 Furniture and Home Furnishings Stores Retail, Large

443 Electronics and Appliance Stores Retail, Large

444 Building Material and Garden Equipment and Supplies Dealers Retail, Large

446 Health and Personal Care Stores Retail, Large

447 Gasoline Stations Retail, Large

448 Clothing and Clothing Accessories Stores Retail, Large

451 Sporting Goods, Hobby, Musical Instrument, and Book Stores Retail, Large

452 General Merchandise Stores Retail, Large

453 Miscellaneous Store Retailers Retail, Large

454 Nonstore Retailers Retail, Large

53211 Passenger Car Rental and Leasing Retail, Large

53212 Truck, Utility Trailer, and RV (Recreational Vehicle) Rental and Leasing Retail, Large

53221 Consumer Electronics and Appliances Rental Retail, Large

53222 Formal Wear and Costume Rental Retail, Large

53223 Video Tape and Disc Rental Retail, Large

53229 Other Consumer Goods Rental Retail, Large

53231 General Rental Centers Retail, Large




71311 Amusement and Theme Parks Retail, Large

71312 Amusement Arcades Retail, Large

71321 Casinos (except Casino Hotels) Retail, Large

71395 Bowling Centers Retail, Large

71399 All Other Amusement and Recreation Industries Retail, Large

812111 Barber Shops Retail, Large

812112 Beauty Salons Retail, Large

812113 Nail Salons Retail, Large

812191 Diet and Weight Reducing Centers Retail, Large

812199 Other Personal Care Services Retail, Large

812210 Funeral Homes and Funeral Services Retail, Large

812220 Cemeteries and Crematories Retail, Large

812310 Coin-Operated Laundries and Drycleaners Retail, Large

6111 Elementary and Secondary Schools School

6114 Business Schools and Computer and Management Training School

6115 Technical and Trade Schools School

6116 Other Schools and Instruction School

6117 Educational Support Services School

481 Air Transportation Warehouse, Non-Refr

483 Water Transportation Warehouse, Non-Refr

484 Truck Transportation Warehouse, Non-Refr

485 Transit and Ground Passenger Transportation Warehouse, Non-Refr

486 Pipeline Transportation Warehouse, Non-Refr

487 Scenic and Sightseeing Transportation Warehouse, Non-Refr

562 Waste Management and Remediation Services Warehouse, Non-Refr

42311 Automobile and Other Motor Vehicle Merchant Wholesalers Warehouse, Non-Refr

42312 Motor Vehicle Supplies and New Parts Merchant Wholesalers Warehouse, Non-Refr

42313 Tire and Tube Merchant Wholesalers Warehouse, Non-Refr

42314 Motor Vehicle Parts (Used) Merchant Wholesalers Warehouse, Non-Refr

42321 Furniture Merchant Wholesalers Warehouse, Non-Refr

42322 Home Furnishing Merchant Wholesalers Warehouse, Non-Refr

42331 Lumber, Plywood, Millwork, and Wood Panel Merchant Wholesalers Warehouse, Non-Refr

42332 Brick, Stone, and Related Construction Material Merchant Wholesalers Warehouse, Non-Refr

42333 Roofing, Siding, and Insulation Material Merchant Wholesalers Warehouse, Non-Refr

42339 Other Construction Material Merchant Wholesalers Warehouse, Non-Refr

42341 Photographic Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr




42342 Office (large/small) Equipment Merchant Wholesalers Warehouse, Non-Refr

42343 Computer and Computer Peripheral Equipment and Software Merchant Wholesalers Warehouse, Non-Refr

42344 Other Commercial Equipment Merchant Wholesalers Warehouse, Non-Refr

42345 Medical, Dental, and Hospital Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42346 Ophthalmic Goods Merchant Wholesalers Warehouse, Non-Refr

42349 Other Professional Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42351 Metal Service Centers and Other Metal Merchant Wholesalers Warehouse, Non-Refr

42352 Coal and Other Mineral and Ore Merchant Wholesalers Warehouse, Non-Refr

42361 Electrical Apparatus and Equipment, Wiring Supplies, and Related Equipment Merchant Wholesalers Warehouse, Non-Refr

42362 Household Appliances, Electric Housewares, and Consumer Electronics Merchant Wholesalers Warehouse, Non-Refr

42369 Other Electronic Parts and Equipment Merchant Wholesalers Warehouse, Non-Refr

42371 Hardware Merchant Wholesalers Warehouse, Non-Refr

42372 Plumbing and Heating Equipment and Supplies (Hydronics) Merchant Wholesalers Warehouse, Non-Refr

42373 Warm Air Heating and Air-Conditioning Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42374 Refrigeration Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42381 Construction and Mining (except Oil Well) Machinery and Equipment Merchant Wholesalers Warehouse, Non-Refr

42382 Farm and Garden Machinery and Equipment Merchant Wholesalers Warehouse, Non-Refr

42383 Industrial Machinery and Equipment Merchant Wholesalers Warehouse, Non-Refr

42384 Industrial Supplies Merchant Wholesalers Warehouse, Non-Refr

42385 Service Establishment Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42386 Transportation Equipment and Supplies (except Motor Vehicle) Merchant Wholesalers Warehouse, Non-Refr

42391 Sporting and Recreational Goods and Supplies Merchant Wholesalers Warehouse, Non-Refr

42392 Toy and Hobby Goods and Supplies Merchant Wholesalers Warehouse, Non-Refr

42393 Recyclable Material Merchant Wholesalers Warehouse, Non-Refr

42394 Jewelry, Watch, Precious Stone, and Precious Metal Merchant Wholesalers Warehouse, Non-Refr

42399 Other Miscellaneous Durable Goods Merchant Wholesalers Warehouse, Non-Refr

42411 Printing and Writing Paper Merchant Wholesalers Warehouse, Non-Refr

42412 Stationery and Office (large/small) Supplies Merchant Wholesalers Warehouse, Non-Refr

42413 Industrial and Personal Service Paper Merchant Wholesalers Warehouse, Non-Refr

42421 Drugs and Druggists' Sundries Merchant Wholesalers Warehouse, Non-Refr

42431 Piece Goods, Notions, and Other Dry Goods Merchant Wholesalers Warehouse, Non-Refr

42432 Men's and Boys' Clothing and Furnishings Merchant Wholesalers Warehouse, Non-Refr




42433 Women's, Children's, and Infants' Clothing and Accessories Merchant Wholesalers Warehouse, Non-Refr

42434 Footwear Merchant Wholesalers Warehouse, Non-Refr

42441 General Line Grocery Merchant Wholesalers Warehouse, Refr

42442 Packaged Frozen Food Merchant Wholesalers Warehouse, Refr

42443 Dairy Product (except Dried or Canned) Merchant Wholesalers Warehouse, Refr

42444 Poultry and Poultry Product Merchant Wholesalers Warehouse, Refr

42445 Confectionery Merchant Wholesalers Warehouse, Refr

42446 Fish and Seafood Merchant Wholesalers Warehouse, Refr

42447 Meat and Meat Product Merchant Wholesalers Warehouse, Refr

42448 Fresh Fruit and Vegetable Merchant Wholesalers Warehouse, Refr

42449 Other Grocery and Related Products Merchant Wholesalers Warehouse, Refr

42451 Grain and Field Bean Merchant Wholesalers Warehouse, Non-Refr

42452 Livestock Merchant Wholesalers Warehouse, Non-Refr

42459 Other Farm Product Raw Material Merchant Wholesalers Warehouse, Non-Refr

42461 Plastics Materials and Basic Forms and Shapes Merchant Wholesalers Warehouse, Non-Refr

42469 Other Chemical and Allied Products Merchant Wholesalers Warehouse, Non-Refr

42471 Petroleum Bulk Stations and Terminals Warehouse, Non-Refr

42472 Petroleum and Petroleum Products Merchant Wholesalers (except Bulk Stations and Terminals) Warehouse, Non-Refr

42481 Beer and Ale Merchant Wholesalers Warehouse, Non-Refr

42482 Wine and Distilled Alcoholic Beverage Merchant Wholesalers Warehouse, Non-Refr

42491 Farm Supplies Merchant Wholesalers Warehouse, Non-Refr

42492 Book, Periodical, and Newspaper Merchant Wholesalers Warehouse, Non-Refr

42493 Flower, Nursery Stock, and Florists' Supplies Merchant Wholesalers Warehouse, Non-Refr

42494 Tobacco and Tobacco Product Merchant Wholesalers Warehouse, Non-Refr

42495 Paint, Varnish, and Supplies Merchant Wholesalers Warehouse, Non-Refr

42499 Other Miscellaneous Nondurable Goods Merchant Wholesalers Warehouse, Non-Refr

49211 Couriers and Express Delivery Services Warehouse, Non-Refr

49221 Local Messengers and Local Delivery Warehouse, Non-Refr

49311 General Warehousing and Storage Warehouse, Non-Refr

49312 Refrigerated Warehousing and Storage Warehouse, Refr

49313 Farm Product Warehousing and Storage Warehouse, Refr

49319 Other Warehousing and Storage Warehouse, Non-Refr

53112 Lessors of Nonresidential Buildings (except Miniwarehouses) Warehouse, Non-Refr

53113 Lessors of Miniwarehouses and Self-Storage Units Warehouse, Non-Refr

811111 General Automotive Repair Warehouse, Non-Refr




811112 Automotive Exhaust System Repair Warehouse, Non-Refr

811113 Automotive Transmission Repair Warehouse, Non-Refr

811118 Other Automotive Mechanical and Electrical Repair and Maintenance Warehouse, Non-Refr

811121 Automotive Body, Paint, and Interior Repair and Maintenance Warehouse, Non-Refr

811122 Automotive Glass Replacement Shops Warehouse, Non-Refr

811191 Automotive Oil Change and Lubrication Shops Warehouse, Non-Refr

811198 All Other Automotive Repair and Maintenance Warehouse, Non-Refr

811310 Commercial and Industrial Machinery and Equipment (except Automotive and Electronic) Repair and Warehouse, Non-Refr

811411 Home and Garden Equipment Repair and Maintenance Warehouse, Non-Refr

812910 Pet Care (except Veterinary) Services Warehouse, Non-Refr

812930 Parking Lots and Garages Warehouse, Non-Refr

CHP POTENTIAL ANALYSIS | E-1

APPENDIX E CHP POTENTIAL ANALYSIS

E.1 Overview We use a “bottom-up” approach to estimate CHP potentials. We begin with state population counts of business establishments from 2012 Census data and of residential units from the 2009 Residential Energy Consumption Survey. We group establishments by common market segment, establishment size, and climate region. These characteristics define the group’s building by size and daily activity. Based on these characteristics, we develop a year of hourly estimates of electrical and thermal end-use loads. We then develop 9 CHP system specifications for each group based on the modeled loads and 5 different CHP system prime movers and two absorption chiller combinations. The technologies are internal combustion (IC) engines, microturbines, gas turbines, heat –recovering fuel cells, and all-electric fuel cells. Absorption chillers are optional except for all-electric fuel cells. Absorption chillers tend to increase CHP technical potential.1 The largest of the generating capacities from these 9 specifications is the group’s average technical potential. We multiply the group’s average technical potential by its population to get its total technical potential.

To identify new technical potential, we must subtract existing CHP capacity from our estimates of total technical potential. We identify existing capacity by market segment and prime mover type. We apportion existing capacity to groups according to their proportions of total technical potential. The remainder after subtraction is new technical potential for each group.

When estimating CHP potentials we consider only specific market segments from the residential, commercial, institutional, and industrial market sectors. We also consider only the 30 to 5000 kW total system capacity market niche. That is the range for which the SGIP provides incentives.2 This market niche effectively excludes most of the industrial sector and the single-family residential segment.

When specifying CHP systems we constrain system operations such that both a minimum number of equivalent full-load operating hours occur annually and a minimum overall fuel efficiency is always achieved. We specify a capacity sufficient to frequently serve but not always satisfy thermal and electric loads. The system capacity, defined in terms of kilowatts of nominal electric generating capacity, depends on daily and seasonal dynamics of a building’s thermal and electric loads.3

We estimate technical potential for a base year of 2012 since the Census data are for that year. We then forecast future technical potentials for groups using the base year estimates and annual growth rates.

1 Absorption chillers increase thermal load but decrease electric load needing to be met. Despite greater thermal load the

decrease in electric load may reduce technical potential due to capacity exceeding electrical load and permissible export. 2 Fuel cells units of 5 kW capacity were supported under the SGIP program. Several single-family residences installed one or

more of these units. Technical problems led to many being removed. While the single-family residential sector has substantial technical potential for small fuel cells we do not expect the under 30 kW market to be a future program target.

3 An all-electric fuel cell system is an exception. Its technical potential depends on electric loads alone. In general, all-electric fuel cells have very similar target audiences to CHP fuel cells whose waste heat is recovered.



The growth rates are based on those published in a 2012 ICF report.4 Table E-1 lists the annual growth rates by market segment.

Table E-1: Market Segment Annual Growth Rates

Segment Annual Growth Rate College 0.63% Food Manufacturing 0.92% Food Store 1.03% Health, Hospital 1.42% Large Multifamily 0.56% Lodging, Hotel 1.26% Office, Large 1.15% Office, Small 1.15% Restaurant, Sit-Down 0.96% Retail, Large 0.13% School 0.63% Warehouse 0.78%

E.2 Market Segment Host Buildings for CHP Systems Estimates of CHP potential depend on the number and sizes of systems. We assume all buildings can host a CHP system of some size. We estimate populations of buildings by market segment and assume one CHP system per building. We estimate the sizes of CHP systems based upon a combination of key characteristics of host buildings described below.

We use 2012 Census data to develop population estimates of commercial, institutional, and industrial sector buildings grouped by key building characteristics. For the residential sector we use 2009 Residential Energy Consumption Survey data. Table E-2 and Table E-3 show the key characteristics and corresponding source data fields from those two data sources.

Table E-2: Commercial and Industrial Census Data Fields

Key Characteristic Census Data Field Building type 2012 North American Industry Classification System (NAICS) code Building conditioned area Employment size bin Climate region and IOU 5-digit ZIP code Number of CHP systems Number of establishments

4 “Combined Heat and Power: Policy Analysis and 2011-2030 Market Assessment,” prepared for the California Energy

Commission, ICF International, Inc., February 2012.



Table E-3: Residential Energy Consumption Survey Data Fields

Key Characteristic RECS Data Field Building conditioned area Unit count in building bin Climate region and IOU State Climate region and IOU Climate region Number of CHP systems Count of units characterized, Unit count in building

Census data provide populations for groups of establishments with common NAICS code, employee count bin, and location. Census data do not provide actual building counts. An establishment may be the sole occupant of a building or it could be one of many establishments in a building. An establishment also may occupy multiple buildings in a campus or complex. We develop building population estimates by assuming an establishment as a proxy for a building.

Where multiple establishments actually occupy a single building the establishment as proxy approach overestimates the building population. This over-counting introduces upward bias on technical potential. In the same case, however, a downward bias is introduced on specification of CHP system capacities. CHP capacities specified for establishments with few employees often are below the minimum technical potential threshold of 30 kW. To recognize certain market segments frequently have buildings occupied by many establishments we include these undersized CHP systems in technical potential estimates. We include such undersized systems for the office and health market segments only. Compared to other market segments, both of these segments have relatively high technical potential and relatively low percentages of buildings with a single establishment. While some portions of the retail market segment also have very low percentages of buildings occupied by one establishment, the segment generally has low technical potential and a high ratio of very small buildings to very large buildings. Table E-4 lists estimated building populations by market segment, climate region, and IOU.



Table E-4: Estimated Building Populations Market Segment, Climate Region, and IOU

PG&E SCE SDG&E

Segment Coastal Inland Coastal Inland Coastal Inland College 89 78 81 118 33 6 Food Manufacturing 542 462 321 454 47 21 Food Store 809 792 750 1,174 278 97 Health, Hospital 777 671 525 895 170 74 Large Multifamily 2,405 1,099 2,198 3,209 970 183 Lodging, Hotel 1,058 379 450 561 237 54 Office, Large 4,347 2,682 4,022 4,796 1,471 338 Office, Small 10,348 8,534 9,581 13,000 3,497 1,111 Restaurant, Sit-Down 8,035 6,716 7,276 10,569 2,916 734 Retail, Large 2,828 2,848 2,848 4,166 1,029 315 Small Multifamily 108,680 33,506 86,901 75,319 22,771 5,116 School 650 376 517 716 192 51

We associate one of 12 non-residential market segments from Itron’s SitePro software with a large number of 2012 NAICS codes. The SitePro market segments are listed here.

» College » Food Manufacturing » Food Store » Healthcare » Large multifamily » Lodging

» Office (large/small) » Restaurant » Retail » Small multifamily » School » Warehouse (refrigerated/not)

Table E-7 at the end of this section lists a complete mapping of market segments and NAICS codes. The specificity of the NAICS codes ranges from 2-digit to 6-digit wide to more exactly associate a SitePro market segment.

We estimate sizes of host buildings in terms of conditioned floor area. We base estimates on midpoints of employment size bin and market segment-specific employee intensities per square foot from SitePro software. For residential multifamily segment we estimate host building size in terms of unit counts per building. We use midpoints from unit count binsin RECS data.

A limitation of Census data used here is an upper limit of employment size bins. The largest is 1,000 or more employees. Establishments with 10,000 employees therefore cannot be distinguished from those with 1,000. For example 2012 Census data show 29 colleges with 1,000 or more employees. To address this limitation we estimate technical potential of either 3 or 5 MW where there are 1,000 or more employees in certain market segments.



We determine host building locations from 5-digit ZIP codes in Census data. We map a climate region, either coastal or inland, as well as an IOU using the ZIP codes. Climate region influences CHP technical and economic potential. IOU influences only economic potential due to different tariffs.

E.3 Building Electrical and Thermal Loads Different building types have different daily routines and so have different electric and thermal load schedules. Larger buildings have larger loads, and buildings in hotter climates have more air conditioning load. We use key characteristics of buildings when estimating electric and thermal loads.

We use Itron’s SitePro software to estimate hourly electric and thermal loads. The load shapes vary depending on market segment, building size, and climate region. We then size CHP systems based on these load shapes and hourly performance minima for the CHP systems.

Using SitePro we develop electric and thermal load shapes for over 500 combinations of market segment, size, and climate. The thermal loads represent heating loads served by conventional natural gas heating service that might be served by CHP. They can include certain electric cooling end-use loads that CHP systems with absorption chillers might serve.

From SitePro’s end-use hourly load models we estimate two annual thermal baseloads for a building- one with and another without inclusion of an absorption chiller as a thermal load. A building with higher and more frequent thermal loads that can be served by CHP has a greater annual thermal baseload and thus has greater technical potential. If electric loads are very low during hours where the thermal load is at or above the annual thermal baseload, however, technical potential may be reduced to limit excessive electric export to the grid.

To include absorption chillers when sizing CHP systems we treat certain electric cooling end-uses as thermal loads. We consider both electric space cooling and refrigeration end-uses among thermal loads for absorption chillers. The absorption chiller then is assumed to reduce the loads of an electric chiller. By treating electric cooling end-uses as thermal loads and including absorption chillers, CHP technical potential may increase. We exclude absorption chillers for the residential sector where there are lower thresholds for CHP system physical footprint and maintenance requirements.

We size CHP systems for specific combinations of prime mover with and without absorption chiller. The sizes are sufficient to serve and, subject to performance and electric export constraints, satisfy the annual thermal baseload. In the exceptional case of all-electric fuel cells it is an annual electric baseload that is satisfied. This combined hourly and annual approach assures annual operating runtime targets and hourly overall fuel efficiency targets both are met.

E.4 CHP Systems Parameters We use several technical parameters to define CHP systems and operations regardless of market segment, building size, or climate region. We size CHP systems to meet specific hourly performance minima while satisfying as much annual thermal load as possible. We assume most generation and all recovered heat are used onsite. These parameters are based on associated performance minima for CHP described in the SGIP Handbook. We begin by assuming:



» a minimum generator electrical capacity of 30 kW, and » achievement of 60 percent HHV overall efficiency (except all-electric fuel cells).

We use a minimum annual operating target in terms of equivalent full load hours (EFLH) when sizing CHP systems. Technical potential is sensitive and inversely related and can be sensitive to this minimum performance parameter. For IC engines, gas turbines, microturbines, and fuel cells with heat recovery we use 5000 EFLH, equivalent to a 57 percent annual capacity factor. For all-electric fuels we use 7000 EFLH, equivalent to an 80 percent annual capacity factor, to recognize typical operation of these prime movers is continuous and very near rated capacity.

A lower EFLH allows for greater technical potential as well as greater operational flexibility. A 5000 EFLH allows CHP systems with multiple independent units to operate in various combinations as loads change while still easily meeting that annual performance minimum. For example, a system composed of five units could have two units remain idle and three units reach 95 percent annual capacity factor and thus achieve 5000 EFLH minimum for the system as a whole. During hours when thermal loads exceed the annual thermal baseload and electrical loads are sufficiently large, the fourth and fifth units also could operate while still meeting hourly performance minima.

We define distinct performance parameters for each of the CHP prime mover technology options. They differ in terms of electrical conversion efficiency as well as power to heat ratio, or how much waste heat is available for every kWh of electricity generated. These differences are important when sizing CHP to serve as much of the annual thermal baseload as possible without generating excess electricity and needing to export beyond a permissible level. Table E-5 lists performance assumptions for the prime mover technologies.

Table E-5: CHP System Performance Assumptions 5

Prime Mover Technology IC Engine Gas Turbine Microturbine

Fuel Cell with Heat Recovery

Fuel Cell All-Electric

Electric Conversion Efficiency (HHV) 31% 24% 24% 42% 52% Heat Recovery Efficiency (HHV) 29% 36% 36% 18% 0% Overall Efficiency (HHV) 60% 60% 60% 60% 52% Hourly Capacity Factor Minimum 50% 50% 50% 80% 80% Annual Equivalent Full Load Hour Minimum 5000 5000 5000 5000 7000 Hourly Generation Export Maximum 25% 25% 25% 25% 25%

All-electric fuel cells have the highest electric conversion efficiency of the prime mover technologies, followed by fuel cells with heat recovery, IC engines, and finally microturbines. These last three technologies share the same overall efficiency assumption, so their heat recovery efficiencies also differ. Their heat recovery efficiencies are simply the difference between the common overall efficiency and their individual electric conversion efficiencies.

5 Source: 2014 EPA CHP Technology Catalog



Table E-2 lists performance assumptions related to absorption chillers and the displacement of electric cooling loads as well as the assumed efficiency of a conventional heating service boiler.

Table E-6: Engineering Performance Assumptions 6

Description Parameter Value Absorption Chiller (single-effect) Coefficient of Performance 0.7 Btu/Btu Electric Chiller Efficiency 0.68 kWh/Ton Electric Space Cooling End-Use Load CHP Displaceable Fraction 100% Electric Refrigeration End-Use Load CHP Displaceable Fraction 50% Conventional Boiler Fuel Efficiency (HHV) 87%

E.5 CHP System Sizing Specifying appropriate CHP system sizes and technologies for various market segments, building sizes, and climate regions is critical to identifying CHP technical potential. We develop specifications in terms of generating capacity in kilowatts and technology in terms of prime mover type as well as whether or not an absorption chiller is included. General and technology-specific CHP system parameters along with performance minima are fit to whole-year hourly thermal and electric load shapes to determine capacities for each prime mover technology. The largest CHP size and its corresponding prime mover define a building’s technical potential.

We estimate annual thermal baseloads from hourly load shapes. We define annual thermal baseload as the 5,000th lowest hourly thermal load. Thermal load includes heating end-use loads that CHP can serve as well as cooling loads where an absorption chiller is included. With this approach there are 5,000 hours in a year that have at a minimum the thermal baseload. CHP systems sized to serve this annual thermal baseload can run at full capacity in all 5,000 hours. This satisfies a performance minimum of 5,000 equivalent full load hours wherein all available heat is used. During hours with lower thermal loads the CHP still may run but at reduced output in order to continue to meet an hourly overall efficiency minimum.

For all-electric fuel cell prime movers we define an annual electric rather than thermal baseload. This is a building’s 7,000th lowest hourly sum of its electric end-use loads. Thus there are 7,000 hours in the year that have at least that total electric load. All-electric fuel cells sized to serve the annual electric baseload can run at full capacity in those 7,000 hours to meet a 7,000 equivalent full load hour performance minimum. This 7,000 hour target recognizes that typical operation of an all-electric fuel cell system or individual fuel cell unit is continuous and very near rated generating capacity, unlike IC engine and microturbine unit operations that may be stopped and restarted or modulated over a wider output range.

A thermally-driven approach to CHP system sizing aims to recover heat sufficient to completely satisfy the annual thermal baseload while at the same time reaching the minimum overall efficiency target. We

6 Ibid.



must reduce system size where satisfying the thermal baseload results in excess electric export to the grid. In such cases the system then does not completely satisfy the annual thermal baseload. Whether or not size is reduced to avoid excess export, heat is recovered sufficient to reach the target overall efficiency.

The strictly electric-driven sizing approach used for all-electric fuel cells aims to satisfy the annual electric baseload. There are no constraints regarding heat recovery and no target overall efficiency besides the electric conversion efficiency of the fuel cell. Despite using the 7,000th lowest electric load as the annual electric baseload, this approach often results in greater technical potential than the thermally-driven approach. The all-electric fuel cell then becomes the prime mover associated with the technical potential.

A few technology and segment-specific details are worth mentioning to better understand how we arrived at these technical potentials. We begin with waste-heat driven absorption chillers and the non-traditional CHP markets of the restaurant and food store segments.

Including an absorption chiller as a thermal load in a CHP system allows space cooling and refrigeration electric end-use loads to be served. This often leads to a higher annual thermal baseload.7 A higher annual thermal baseload can increase CHP technical potential. The restaurant and food store segments both have fairly large refrigeration and space cooling electric end-use loads. The dark blue upper band across Figure E-1 shows that, on an average weekday, refrigeration is the largest electrical end-use load for a food store. The lighter blue band just below refrigeration is the third largest load, space cooling. Both these electric end-use loads can be served by an absorption chiller. A number of food store pilot projects recently have demonstrated success with the addition of absorption chillers to serve cooling and refrigeration loads.8

7 Annual thermal baseload is a metric of hourly thermal needs reached in no less than a minimum number of hours per year.

In this analysis it is used to define target thermal output capacity of a CHP system. 8 CHP project reports on food store are available at these web pages

http://files.harc.edu/Sites/GulfCoastCHP/CaseStudies/WaldbaumsHauppagueNY.pdf

http://files.harc.edu/Sites/GulfCoastCHP/CaseStudies/APFreshMarketMountKiskoNY.pdf

http://www.pewtrusts.org/en/research-and-analysis/fact-sheets/2015/06/combined-heat-and-power-helps-groceries-reduce-costs-and-increase-resiliency

http://www.nyserda.ny.gov/About/Publications/Case-Studies/NCP-Case-Studies/Whole-Foods-Market.aspx

http://files.harc.edu/Sites/GulfCoastCHP/CaseStudies/WaldbaumsHauppagueNY.pdf

http://files.harc.edu/Sites/GulfCoastCHP/CaseStudies/APFreshMarketMountKiskoNY.pdf





Figure E-1: Food Store Segment Annual Average Weekday Hourly Electric End-Use Loads

The health care segment is a traditional CHP target because of its characteristically high and year round thermal loads for domestic hot water as well as its high summertime space cooling electric loads. Although it has fewer numbers than food stores, the health segment has larger buildings. Figure E-2 shows an average weekday’s hourly natural gas end-use loads for a health facility. Figure E-3 shows corresponding hourly electric end-use loads for a health facility.



Figure E-2: Health Segment Average Annual Weekday Hourly Natural Gas End Use Loads

Figure E-3: Health Segment Average Annual Weekday Hourly Electric End Use Loads



For the health segment we sized CHP thermally to serve hot water and space heating natural gas end-uses shown by the bright yellow and red bands respectively in Figure E-2. By including absorption chillers as thermal loads we also sized CHP thermally to serve space cooling and refrigeration electric end-uses shown by light and dark blue bands in Figure E-3. Any electric end-use of course can by served by CHP’s electric generation.

Cont ribut ion of Absorpt ion Chilling to Technical Potent ial Like electric chillers, absorption chillers serve cooling loads but unlike other chillers they are fueled by heat. An absorption chiller can be an additional thermal load for a CHP system to serve. This can increase a building’s CHP technical potential. By displacing electric chiller operation, however, an absorption chiller reduces electric loads. Reduced electric loads potentially reduce technical potential.

To determine the effect on technical potential of including an optional absorption chiller as a thermal load, we specified non-residential sector CHP systems both with and without absorption chillers. Large and small multifamily market segments were sized without absorption chilling. Figure E-4 shows technical potential with absorption as a system option and with no absorption included for the various market segments.

Figure E-4: Technical Potential with and without Absorption Chilling Option



All non-residential segments had at least some systems where absorption chilling increased technical potential. The largest effects were in the food store, restaurant, and retail segments. The addition of an absorption chiller option increased total technical potential by more than 35%.

For absorption chiller performance we could assume a single-effect or a double-effect system. Single-effect is less efficient at cooling, and also less expensive, than double-effect. The coefficients of performance are 0.7 and 1.1 for single and double respectively. We assumed the less efficient absorption chiller so possible increases in annual thermal load would be greater. This assumption also served to increase technical potential compared to the more efficient chiller. The less efficient chiller’s lower costs meanwhile might increase economic potential.

Inf luence of Annual Operat ing Hour R equirem ent To avoid having excess generating capacity that gets little use over the course of a year we specify CHP systems using annual performance minima. We express these in terms of equivalent full load hours (EFLH) of operation. They also can be expressed in terms of annual capacity factors. The SGIP’s performance-based incentive schedule assumes CHP to have an 80 percent annual capacity factor. An 80% annual capacity factor is equivalent to 7,000 EFLH.9

As an operating minimum 7,000 EFLH reduces technical potential relative to lower EFLH. This is because to run more hours at full load while maintaining high overall efficiency the CHP capacity must be smaller. We assumed a less conservative minimum of 5,000 EFLH for all prime mover technologies except all-electric fuel cells. This assumption served to increase technical potential compared to a higher minimum value. Figure E-5 shows differences in total technical potential using 5,000, 6,000, and 7,000 EFLH as annual operating hour constraints.

9 Annual capacity factor is the quotient of the equivalent full load hour value divided by 8760 hours per year.



0

500

1,000

1,500

2,000

2,500

3,000

Tota

l Tec

hnic

al P

oten

tial (

MW

)

5000 EFLH 6000 EFLH 7000 EFLH

Figure E-5: Technical Potential under Different Annual Operating Hour Constraints

The vertical axis of Figure E-5 is truncated to better show differences in segments apart from the food manufacturing segment where different annual minimum made no difference in technical potential. Figure E-5 shows that increasing the annual minimum from 5,000 to 6,000 EFLH reduces total technical potential dramatically in food store, retail, and restaurant segments. An increase from 6,000 to 7,000 further reduces potential in those segments as well as in the lodging segment.

Com parisons w ith Earlier St udies We compare results of this analysis with results of two earlier studies that described CHP technical potential in California. Both studies examined the CHP market for a wider range of system capacity than the 30 kW to 5 MW range considered in this analysis. They considered technical potential of CHP with capacities from 50 kW to above 100 MW. In making comparisons to those studies we account for the different market segments and maximum capacities considered.10 Both studies divide CHP markets into system capacity bins and market segments such that reasonable comparisons can be made with this analysis.

10 This analysis of CHP potentials is preceded by earlier studies “Assessment of California CHP Market and Policy Options for

Increased Penetration,” Electric Power Research Institute, prepared for the California Energy Commission, CEC-500-2005-060, April 2005; and “Combined Heat And Power: Policy Analysis and 2011-2030 Market Assessment,” ICF International, prepared for the California Energy Commission, CEC-200-2012-002, February 2012.. These prior studies included estimates of technical and market potential but examined a wider range of CHP system sizes than the 30 kW to 5000 kW range examined here.



The estimate developed here is much larger than the 4,123 MW estimated in a 2005 EPRI study11 for an approximately equivalent group of commercial segments.12 The EPRI study did not size systems based upon hourly end-use load shape data. It used annual electric and gas consumption by market segment.

A second, later study provides greater market segment detail and so allows for more thorough comparison. ICF estimated technical potential of 3,522 MW in 2011 for an equivalent group of market segments in its study.13 Figure E-6 above shows the segments and technical potentials common to both the ICF and this study.

Figure E-6: Comparisons of Market Segment Technical Potentials, ICF 2011 and Itron

11 “Assessment of California CHP Market and Policy Options for Increased Penetration,” Prepared by EPRI for CEC PIER, CEC-

500-2005-060-D, April 2005. 12 EPRI’s ‘CCHP market’ includes absorption chilling and in that sense is equivalent only to the commercial sector addressed by

this study since residential and industrial sectors are presumed without absorption chilling. 13 “Combined Heat and Power: Policy Analysis and 2011-2030 Market Assessment,” prepared for the California Energy

Commission, ICF International, Inc., February 2012. We included system sizes up to 5 MW from tables C-3 to C-8 of the ICF report and the following combinations of NAICS/Application: 311-312/Food, 452/Retail, 493/Refrigerated Warehouses, 445/Food Stores, 722/Restaurants, 531/Commercial Buildings , 531/Multifamily Buildings , 721/Hotels , 518/Data Centers , 512131/Movie Theaters , 71394/Health Clubs , 71391/Golf/Country Clubs , 623/Nursing Homes , 622/Hospitals , 6111/Schools , 6113/College/Univ. , 612/Museums

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

New

Tech

nica

l Pot

entia

l (M

W)

ICF 2011 ITRON



ICF found the greatest technical potential to be in the large office segment and found little potential in the food store segment. ICF’s estimate for the traditional CHP market of the health segment was substantial yet less than for the food manufacturing and lodging segments.

Differences in technical potential estimates can arise from different approaches to specifying CHP systems as well as from different population counts of buildings. It is not clear which difference may be more influential in any of the market segments. Below we discuss both areas of difference and their possible contributions.

Differences in CHP system sizing approaches influence technical potential. This study used a 30 kW threshold capacity for technical potential whereas ICF used a 50 kW. ICF did not count facilities with systems sized between 30 and 49 kW. Almost 13 percent of this study’s estimated potential is from systems sized between 30 and 49 kW.

Different CHP system sizing approaches can include different minimum numbers of annual hours of operation. This study required a minimum number of equivalent full load hours from every CHP system. For CHP systems we required a minimum 5,000 EFLH. ICF segmented the CHP market in several ways including in terms of high or low annual ‘load factor’ that indicated the number of hours of annual operation. ICF used load factors of 7,500 and 5,000 hours depending on market segment.

This analysis differs from ICF’s in terms of the time resolution of data underlying CHP system sizing. Here we use SitePro hourly end use load shapes. ICF used annual and monthly resolution data from DOE’s Commercial Buildings Energy Consumption Survey (CBECS) and Manufacturing Energy Consumption Survey (MECS), IHS’s Commercial Energy Profile Database (CEPD), and for the commercial sector, the California Commercial End-Use Survey (CEUS). CEUS was used to make annual thermal demand estimates more indicative of a California climate. ICF’s modeling may have suggested smaller annual thermal baseloads than this analysis did. CHP sizing then would be correspondingly smaller.

Another difference between studies is the counting of building populations. ICF’s primary data source is the Dun & Bradstreet Hoovers Database acquired in October 2011. Itron uses 2012 Census establishment-level data. The potential for differences between these sources is not clear.



Table E-7: Commercial and Industrial 2012 NAICS Codes and Associated SitePro Building Type

NAICS Code NAICS Description SitePro Building Type14

6112 Junior Colleges College

6113 Colleges, Universities, and Professional Schools College

311 Food Manufacturing Food Manufacturing

312 Beverage and Tobacco Product Manufacturing Food Manufacturing

445 Food and Beverage Stores Food Store

622 Hospitals Health, Hospital

623 Nursing and Residential Care Facilities Health, Hospital

721 Accommodation Lodging, Hotel

71391 Golf Courses and Country Clubs Lodging, Hotel

71394 Fitness and Recreational Sports Centers Lodging, Hotel

92214 Correctional institutions Lodging, Hotel

53111 Lessors of Residential Buildings and Dwellings MultiFamily

23 Construction Office (large/small)

52 Finance and Insurance Office (large/small)

54 Professional, Scientific, and Technical Services Office (large/small)

55 Management of Companies and Enterprises Office (large/small)

488 Support Activities for Transportation Office (large/small)

511 Publishing Industries (except Internet) Office (large/small)

512 Motion Picture and Sound Recording Industries Office (large/small)

515 Broadcasting (except Internet) Office (large/small)

517 Telecommunications Office (large/small)

518 Data Processing, Hosting, and Related Services Office (large/small)

519 Other Information Services Office (large/small)

561 Administrative and Support Services Office (large/small)

621 Ambulatory Health Care Services Office (large/small)

624 Social Assistance Office (large/small)

921 Executive, legislative and general government Office (large/small)

923 Administration of human resource programs Office (large/small)

924 Administration of environmental programs Office (large/small)

925 Community and housing program administration Office (large/small)

926 Administration of economic programs Office (large/small)

927 Space research and technology Office (large/small)

928 National security and international affairs Office (large/small)

14 Office is deemed large or small based on Census employee count bin.




42511 Business to Business Electronic Markets Office (large/small)

42512 Wholesale Trade Agents and Brokers Office (large/small)

53119 Lessors of Other Real Estate Property Office (large/small)

53121 Office (large/small)s of Real Estate Agents and Brokers Office (large/small)

53131 Real Estate Property Managers Office (large/small)

53132 Office (large/small)s of Real Estate Appraisers Office (large/small)

53139 Other Activities Related to Real Estate Office (large/small)

53241 Construction, Transportation, Mining, and Forestry Machinery and Equipment Rental and Leasing Office (large/small)

53242 Office (large/small) Machinery and Equipment Rental and Leasing Office (large/small)

53249 Other Commercial and Industrial Machinery and Equipment Rental and Leasing Office (large/small)

53311 Lessors of Nonfinancial Intangible Assets (except Copyrighted Works) Office (large/small)

71131 Promoters of Performing Arts, Sports, and Similar Events with Facilities Office (large/small)

71132 Promoters of Performing Arts, Sports, and Similar Events without Facilities Office (large/small)

71141 Agents and Managers for Artists, Athletes, Entertainers, and Other Public Figures Office (large/small)

71151 Independent Artists, Writers, and Performers Office (large/small)

71211 Museums Office (large/small)

71212 Historical Sites Office (large/small)

71329 Other Gambling Industries Office (large/small)

92211 Courts Office (large/small)

92212 Police protection Office (large/small)

92213 Legal counsel and prosecution Office (large/small)

811211 Consumer Electronics Repair and Maintenance Office (large/small)

811212 Computer and Office (large/small) Machine Repair and Maintenance Office (large/small)

811213 Communication Equipment Repair and Maintenance Office (large/small)

811219 Other Electronic and Precision Equipment Repair and Maintenance Office (large/small)

811412 Appliance Repair and Maintenance Office (large/small)

811420 Reupholstery and Furniture Repair Office (large/small)

811430 Footwear and Leather Goods Repair Office (large/small)

811490 Other Personal and Household Goods Repair and Maintenance Office (large/small)

812921 Photofinishing Laboratories (except One-Hour) Office (large/small)

812922 One-Hour Photofinishing Office (large/small)




812990 All Other Personal Services Office (large/small)

813110 Religious Organizations Office (large/small)

813211 Grantmaking Foundations Office (large/small)

813212 Voluntary Health Organizations Office (large/small)

813219 Other Grantmaking and Giving Services Office (large/small)

813311 Human Rights Organizations Office (large/small)

813312 Environment, Conservation and Wildlife Organizations Office (large/small)

813319 Other Social Advocacy Organizations Office (large/small)

813410 Civic and Social Organizations Office (large/small)

813910 Business Associations Office (large/small)

813920 Professional Organizations Office (large/small)

813930 Labor Unions and Similar Labor Organizations Office (large/small)

813940 Political Organizations Office (large/small)

813990 Other Similar Organizations (except Business, Professional, Labor, and Political Organizations) Office (large/small)

722 Food Services and Drinking Places Restaurant, Sit-Down

71111 Theater Companies and Dinner Theaters Restaurant, Sit-Down

441 Motor Vehicle and Parts Dealers Retail, Large

442 Furniture and Home Furnishings Stores Retail, Large

443 Electronics and Appliance Stores Retail, Large

444 Building Material and Garden Equipment and Supplies Dealers Retail, Large

446 Health and Personal Care Stores Retail, Large

447 Gasoline Stations Retail, Large

448 Clothing and Clothing Accessories Stores Retail, Large

451 Sporting Goods, Hobby, Musical Instrument, and Book Stores Retail, Large

452 General Merchandise Stores Retail, Large

453 Miscellaneous Store Retailers Retail, Large

454 Nonstore Retailers Retail, Large

53211 Passenger Car Rental and Leasing Retail, Large

53212 Truck, Utility Trailer, and RV (Recreational Vehicle) Rental and Leasing Retail, Large

53221 Consumer Electronics and Appliances Rental Retail, Large

53222 Formal Wear and Costume Rental Retail, Large

53223 Video Tape and Disc Rental Retail, Large

53229 Other Consumer Goods Rental Retail, Large

53231 General Rental Centers Retail, Large




71311 Amusement and Theme Parks Retail, Large

71312 Amusement Arcades Retail, Large

71321 Casinos (except Casino Hotels) Retail, Large

71395 Bowling Centers Retail, Large

71399 All Other Amusement and Recreation Industries Retail, Large

812111 Barber Shops Retail, Large

812112 Beauty Salons Retail, Large

812113 Nail Salons Retail, Large

812191 Diet and Weight Reducing Centers Retail, Large

812199 Other Personal Care Services Retail, Large

812210 Funeral Homes and Funeral Services Retail, Large

812220 Cemeteries and Crematories Retail, Large

812310 Coin-Operated Laundries and Drycleaners Retail, Large

6111 Elementary and Secondary Schools School

6114 Business Schools and Computer and Management Training School

6115 Technical and Trade Schools School

6116 Other Schools and Instruction School

6117 Educational Support Services School

481 Air Transportation Warehouse, Non-Refr

483 Water Transportation Warehouse, Non-Refr

484 Truck Transportation Warehouse, Non-Refr

485 Transit and Ground Passenger Transportation Warehouse, Non-Refr

486 Pipeline Transportation Warehouse, Non-Refr

487 Scenic and Sightseeing Transportation Warehouse, Non-Refr

562 Waste Management and Remediation Services Warehouse, Non-Refr

42311 Automobile and Other Motor Vehicle Merchant Wholesalers Warehouse, Non-Refr

42312 Motor Vehicle Supplies and New Parts Merchant Wholesalers Warehouse, Non-Refr

42313 Tire and Tube Merchant Wholesalers Warehouse, Non-Refr

42314 Motor Vehicle Parts (Used) Merchant Wholesalers Warehouse, Non-Refr

42321 Furniture Merchant Wholesalers Warehouse, Non-Refr

42322 Home Furnishing Merchant Wholesalers Warehouse, Non-Refr

42331 Lumber, Plywood, Millwork, and Wood Panel Merchant Wholesalers Warehouse, Non-Refr

42332 Brick, Stone, and Related Construction Material Merchant Wholesalers Warehouse, Non-Refr

42333 Roofing, Siding, and Insulation Material Merchant Warehouse, Non-Refr



NAICS Code NAICS Description SitePro Building Type14 Wholesalers

42339 Other Construction Material Merchant Wholesalers Warehouse, Non-Refr

42341 Photographic Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42342 Office (large/small) Equipment Merchant Wholesalers Warehouse, Non-Refr

42343 Computer and Computer Peripheral Equipment and Software Merchant Wholesalers Warehouse, Non-Refr

42344 Other Commercial Equipment Merchant Wholesalers Warehouse, Non-Refr

42345 Medical, Dental, and Hospital Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42346 Ophthalmic Goods Merchant Wholesalers Warehouse, Non-Refr

42349 Other Professional Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42351 Metal Service Centers and Other Metal Merchant Wholesalers Warehouse, Non-Refr

42352 Coal and Other Mineral and Ore Merchant Wholesalers Warehouse, Non-Refr

42361 Electrical Apparatus and Equipment, Wiring Supplies, and Related Equipment Merchant Wholesalers Warehouse, Non-Refr

42362 Household Appliances, Electric Housewares, and Consumer Electronics Merchant Wholesalers Warehouse, Non-Refr

42369 Other Electronic Parts and Equipment Merchant Wholesalers Warehouse, Non-Refr

42371 Hardware Merchant Wholesalers Warehouse, Non-Refr

42372 Plumbing and Heating Equipment and Supplies (Hydronics) Merchant Wholesalers Warehouse, Non-Refr

42373 Warm Air Heating and Air-Conditioning Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42374 Refrigeration Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42381 Construction and Mining (except Oil Well) Machinery and Equipment Merchant Wholesalers Warehouse, Non-Refr

42382 Farm and Garden Machinery and Equipment Merchant Wholesalers Warehouse, Non-Refr

42383 Industrial Machinery and Equipment Merchant Wholesalers Warehouse, Non-Refr

42384 Industrial Supplies Merchant Wholesalers Warehouse, Non-Refr

42385 Service Establishment Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr

42386 Transportation Equipment and Supplies (except Motor Vehicle) Merchant Wholesalers Warehouse, Non-Refr

42391 Sporting and Recreational Goods and Supplies Merchant Wholesalers Warehouse, Non-Refr

42392 Toy and Hobby Goods and Supplies Merchant Wholesalers Warehouse, Non-Refr

42393 Recyclable Material Merchant Wholesalers Warehouse, Non-Refr




42394 Jewelry, Watch, Precious Stone, and Precious Metal Merchant Wholesalers Warehouse, Non-Refr

42399 Other Miscellaneous Durable Goods Merchant Wholesalers Warehouse, Non-Refr

42411 Printing and Writing Paper Merchant Wholesalers Warehouse, Non-Refr

42412 Stationery and Office (large/small) Supplies Merchant Wholesalers Warehouse, Non-Refr

42413 Industrial and Personal Service Paper Merchant Wholesalers Warehouse, Non-Refr

42421 Drugs and Druggists' Sundries Merchant Wholesalers Warehouse, Non-Refr

42431 Piece Goods, Notions, and Other Dry Goods Merchant Wholesalers Warehouse, Non-Refr

42432 Men's and Boys' Clothing and Furnishings Merchant Wholesalers Warehouse, Non-Refr

42433 Women's, Children's, and Infants' Clothing and Accessories Merchant Wholesalers Warehouse, Non-Refr

42434 Footwear Merchant Wholesalers Warehouse, Non-Refr

42441 General Line Grocery Merchant Wholesalers Warehouse, Refriger

42442 Packaged Frozen Food Merchant Wholesalers Warehouse, Refriger

42443 Dairy Product (except Dried or Canned) Merchant Wholesalers Warehouse, Refriger

42444 Poultry and Poultry Product Merchant Wholesalers Warehouse, Refriger

42445 Confectionery Merchant Wholesalers Warehouse, Refriger

42446 Fish and Seafood Merchant Wholesalers Warehouse, Refriger

42447 Meat and Meat Product Merchant Wholesalers Warehouse, Refriger

42448 Fresh Fruit and Vegetable Merchant Wholesalers Warehouse, Refriger

42449 Other Grocery and Related Products Merchant Wholesalers Warehouse, Refriger

42451 Grain and Field Bean Merchant Wholesalers Warehouse, Non-Refr

42452 Livestock Merchant Wholesalers Warehouse, Non-Refr

42459 Other Farm Product Raw Material Merchant Wholesalers Warehouse, Non-Refr

42461 Plastics Materials and Basic Forms and Shapes Merchant Wholesalers Warehouse, Non-Refr

42469 Other Chemical and Allied Products Merchant Wholesalers Warehouse, Non-Refr

42471 Petroleum Bulk Stations and Terminals Warehouse, Non-Refr

42472 Petroleum and Petroleum Products Merchant Wholesalers (except Bulk Stations and Terminals) Warehouse, Non-Refr

42481 Beer and Ale Merchant Wholesalers Warehouse, Non-Refr

42482 Wine and Distilled Alcoholic Beverage Merchant Wholesalers Warehouse, Non-Refr

42491 Farm Supplies Merchant Wholesalers Warehouse, Non-Refr




42492 Book, Periodical, and Newspaper Merchant Wholesalers Warehouse, Non-Refr

42493 Flower, Nursery Stock, and Florists' Supplies Merchant Wholesalers Warehouse, Non-Refr

42494 Tobacco and Tobacco Product Merchant Wholesalers Warehouse, Non-Refr

42495 Paint, Varnish, and Supplies Merchant Wholesalers Warehouse, Non-Refr

42499 Other Miscellaneous Nondurable Goods Merchant Wholesalers Warehouse, Non-Refr

49211 Couriers and Express Delivery Services Warehouse, Non-Refr

49221 Local Messengers and Local Delivery Warehouse, Non-Refr

49311 General Warehousing and Storage Warehouse, Non-Refr

49312 Refrigerated Warehousing and Storage Warehouse, Refriger

49313 Farm Product Warehousing and Storage Warehouse, Refriger

49319 Other Warehousing and Storage Warehouse, Non-Refr

53112 Lessors of Nonresidential Buildings (except Miniwarehouses) Warehouse, Non-Refr

53113 Lessors of Miniwarehouses and Self-Storage Units Warehouse, Non-Refr

811111 General Automotive Repair Warehouse, Non-Refr

811112 Automotive Exhaust System Repair Warehouse, Non-Refr

811113 Automotive Transmission Repair Warehouse, Non-Refr

811118 Other Automotive Mechanical and Electrical Repair and Maintenance Warehouse, Non-Refr

811121 Automotive Body, Paint, and Interior Repair and Maintenance Warehouse, Non-Refr

811122 Automotive Glass Replacement Shops Warehouse, Non-Refr

811191 Automotive Oil Change and Lubrication Shops Warehouse, Non-Refr

811198 All Other Automotive Repair and Maintenance Warehouse, Non-Refr

811310 Commercial and Industrial Machinery and Equipment (except Automotive and Electronic) Repair and Warehouse, Non-Refr

811411 Home and Garden Equipment Repair and Maintenance Warehouse, Non-Refr

812910 Pet Care (except Veterinary) Services Warehouse, Non-Refr

812930 Parking Lots and Garages Warehouse, Non-Refr

CONSULTING AND ANALYSIS

330 Madson Pl. Davis, CA 95618

Phone: 1.800.635.5461

Join us in creating a more resourceful world.

To learn more visit itron.com/consulting

Market Transformation: Final Report

Documents

Transcript of Market Transformation: Final Report