Energy Solutions June 2009

California 33% RPS

Presentation to the SF PES Chapter

byBarney Speckman

and Anu Pandey

1

Overview

What is the 33% Renewable Portfolio Standard (RPS)How would it workHow might the 33% RPS requirements be metWhat are some of the challenges What are the opportunitiesQ&A

2

What is the 33% RPS

AB 32 (2006) Requires California to reduce GHG emissions to 1990 levels by 2020California Air Resources Board (CARB) has been assigned task of developing a plan to implement AB 32CARB’s proposed plan includes establishing a 33% Renewable Portfolio Standard for the California electric industryCurrently a mandatory 20% RPS (by 2010) is in place for the states investor owned utilities (PG&E, SCE, SDG&E) with many Municipal Utilities voluntarily implementing RPS targets

3

How would it work

Would require 33% of the amount of energy sold to customers to be produced by eligible renewable resourcesApplies to all companies selling energy at retail in CASets 2020 as date to meet the 33% StandardEstablishes penalties for non complianceLegislation being discussed in Sacramento

4

How might the 33% RPS be met

No none knows for certain!!!

Many degrees of freedom in the implementation thus many uncertainties will have to be dealt withWhat technologies will utilized?Where, when and what plants will be built?How will the power be delivered to customers?What other infrastructure will be needed?

Several studies have/are being conducted

5

Possible 33% Renewable Futures Recent CPUC study used to consider several possible futures as a way to bracket the future implementation of the 33% RPSExamines cost, difficulty, GHG reductions for several mixes of technology and infrastructure requirementsLooks at:– 33% Reference– High Wind– High Imports – High Distributed Generation

6

Possible 33% Futures (Cont’d)

Energy and Capacity Requirements based upon CPUC Forecast compared to 2007

Renewable Portfolio Standard

Additional Energy Required

Additional Renewable

Capacity Required (Approx)

20% RPS 35 TWh 10,000 MWs

33% RPS 75 TWh 22,000 MWs

2007 Renewable Energy was 27 TWh

TWh = 1012 Watt-hours

7

Lets Look at the Technologies in California

Major contributors (potentially) to new production– Wind– Solar Thermal – Solar PV– Geothermal

Lesser contributors to new production– Biomass – Biogas – Small Hydro

Majority of new renewables in California are in Southern part of the state

8

New Renewable Additions Under CPUC Cases

High Out Of State Delivered High Wind

Distributed Generation

Biogas 279 279 279Biomass 575 634 403Geothermal 1,655 1,655 1,415Solar PV 969 1,162 15,068Solar Thermal 2,101 3,163 1,095Wind 5,756 9,575 4,484Sub Total 11,357 16490 22,766

Biomass 87 87 87Geothermal 938 58 58Solar Thermal 534 534 534Wind 6,910 4,802 3,302Sub Total 8,496 5,496 3,996

Total 19,853 21,986 26,762

California

Out of State

9

Nexant Study - Assumed Locations of Incremental Renewables in California - 33% RPS, 2020

Excluded areas with high population density, national parks, landmarks, sensitive areas, etc., to exclude areas not practical for renewable project development

Geothermal 135Siskiyou

Solar 2303Wind 750

San Bernardino

Solar 1250Wind 500

Riverside

Solar 750

Imperial

Wind 513

San Diego/ Imperial

Biomass 100Solar 2250Wind 2610

LA/Kern

Biomass 100Humboldt

BiomassWind

200350

Bay Area

Solar 177San Luis Obispo

10

Lets Look at the Technologies in CaliforniaWind Generation

11

Wind Generation Characteristics

Low cost technology on an energy basisProduction is – Variable– Uncertain– Often Remotely Located– Not highly correlated in time with system load

Capacity credit 10-30% of nameplate for long range planning purposes

12

Tehachapi Wind Generation in April – 2005Variable and Uncertain

0

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Meg

awat

ts

−Average

Each Day is a different color.

−Day 29

−Day 5−Day 26

−Day 9

CAISO Data

13

Wind generation tends to be inversely correlated to daily system load

CAISO Load -- Fall 2006

20,00021,00022,00023,00024,00025,00026,00027,00028,00029,00030,00031,00032,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hours

MW

Load

Total Wind -- Fall 2006

300325350375400425450475500525550575600

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hours

MW

Total Wind

CAISO Data

14

Wind vs. Actual Load on a Typical Hot Day in 2006

CAISO Wind GenerationJuly 2006 Heat Wave

0

200

400

600

800

1,000

1,200

07/1

6/06

07/1

7/06

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2/06

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07/2

4/06

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5/06

07/2

5/06

07/2

6/06

07/2

6/06

MW

Wind Generation Wind Generation at Peak

Total Wind Generation Installed Capacity = 2,648 MW

Wind Generation at Peak

CAISO Data

15

Wind Generation Summer 2007 – 5 Hottest Days

CAISO Load Data

CAISO Data

16

Lets Look at the Technologies in CaliforniaSolar Characteristics

Higher cost on an energy basisSeveral technologies– Thermal (central tower, trough, Sterling, etc)– Thermal with storage or supplemental gas firing– PV roof top– PV large scale (> 1MW)

Insolation is variable but absent clouds relatively certainWith clouds, production is less certainSolar production correlates with system loadTechnologies with larger thermal mass tend to filter out short term variability (e.g solar thermal)Solar Capacity Credit 60% – 95% (depending upon technology) for the purpose of long range planning

17

Lets Look at the Technologies in CaliforniaSolar Thermal Generation

eSolar’s Modular Solar Power Plant Concept

Solar II Solar Central Receiver

Trough Design

18

Lets Look at the Technologies in CaliforniaSolar PV

19

Solar Insolation Examples – Sacramento Area

Spring 2009 data collected at SMUD PV siteData is collected at 1 minute intervalsSeveral days shown in first week of March and MayIndicative of Solar production, especially PV

Source: http://www.nrel.gov/midc/

20

Solar Insolation Examples – SacramentoMarch 7

March 7, 2009

0

100

200

300

400

500

600

700

800

900

6:30

6:48

7:06

7:24

7:42

8:00

8:18

8:36

8:54

9:12

9:30

9:48

10:06

10:24

10:42

11:00

11:18

11:36

11:54

12:12

12:30

12:48

13:06

13:24

13:42

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14:18

14:36

14:54

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15:48

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16:24

16:42

17:00

17:18

17:36

17:54

Time

Glob

al Ho

rizon

tal [W

/m^2

]

21

Solar Insolation Examples – SacramentoMarch 5

March 5, 2009

0

100

200

300

400

500

600

700

800

900

1000

6:33

6:51

7:09

7:27

7:45

8:03

8:21

8:39

8:57

9:15

9:33

9:51

10:09

10:27

10:45

11:03

11:21

11:39

11:57

12:15

12:33

12:51

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13:27

13:45

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14:21

14:39

14:57

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15:33

15:51

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16:27

16:45

17:03

17:21

17:39

17:57

Time

Glob

al Ho

rizon

tal [W

/m^2

]

22

Solar Insolation Examples – SacramentoMarch 2

March 2, 2009

0

100

200

300

400

500

600

700

800

900

1000

6:38

6:56

7:14

7:32

7:50

8:08

8:26

8:44

9:02

9:20

9:38

9:56

10:14

10:32

10:50

11:08

11:26

11:44

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12:20

12:38

12:56

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13:32

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14:26

14:44

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15:56

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16:32

16:50

17:08

17:26

17:44

Time

Glob

al Ho

rizon

tal [W

/m^2

]

23

Solar Insolation Examples – SacramentoMay 5

May 5, 2009

0

200

400

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1400

5:05

5:23

5:41

5:59

6:17

6:35

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15:35

15:53

16:11

16:29

Time

Glob

al Ho

rizon

tal [W

/m^2

]

24

Typical Daily Wind vs. Solar Generation PatternShows Complimentary Nature

Wind vs SolarMay 25, 2007

-10

190

390

590

790

990

1190

1390

00:0

0

00:4

5

01:3

0

02:1

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00:0

0

MW

Wind Solar

CAISO Data

25

System Operations – Renewable Integration

Regulation (second to second Auto Generation Control) and Ramping ( minute to minute) requirements affected by amount of renewables and the typeUnit commitment to cover the peak plus reservesVariability and Uncertainty (forecast error) contribute to increased requirementsNexant study results

26

System Operations – Renewable Integration RequirementsRegulation and Ramping Nexant Study

Includes effects of load and wind only (not solar); results understates needs

Regulation for 33% RPS in 2020 = 541 MW Up, 559 MW Down, vs. +/- 350 MW today

CAISO 2006

CAISO 20% 2013

Statewide 20% 2025

Statewide 33% 2025

Winter 6,979 8,631 10,243 12,184Spring 6,860 8,494 10,967 13,272Summer 10,090 12,664 17,823 18,727Fall 7,229 8,995 11,016 11,950

Morning Ramps

CAISO 2006

CAISO 20% 2013

Statewide 20% 2025

Statewide 33% 2025

Winter 7,856 9,293 12,090 13,483Spring 7,962 9,783 11,854 13,497Summer 10,589 12,135 15,938 16,330Fall 11,511 13,483 14,167 15,851

Evening Ramps

27

System Operations – Renewable Integration

Over-generation potential increases with renewablesOvergen occurs when inflexible generation exceeds load plus planned exports“Energy Dump” occurs when over-gen can not be sold to willing buyers in neighboring areasNexant studies indicate– Technology dependent– With high solar penetration can occur during high load

hours

28

System Operations – Renewable IntegrationOvergen/Dump Energy – Nexant Results

Most likely to happen in March-May period when hydro, wind and solar production can all be high and on weekends when load is lowIn simulations, more than 90% of dump occurs in SCE territorySimulation understates dump due to simplified transmission model used in production simulation and normal hydro conditions assumedMay require changes in current and future contract structure to allow more frequent curtailment, as well as needing to reduce minimum generation levels

29

Results – Dump Energy for 50% RPS

05,000

10,000

15,00020,00025,00030,000

35,00040,00045,00050,000

55,00060,000

1 3 5 7 9 11 13 15 17 19 21 23

Hour

MW

05,000

10,00015,00020,000

25,00030,00035,000

40,00045,00050,000

55,00060,000

1 3 5 7 9 11 13 15 17 19 21 23

Hour

MW

Without Storage With Storage (6 hours)

Storage Technologies reduce dump energy

Conditions on a day with solar and wind conditions both high… 50% RPS

WindSolarTraditionalLoad+ExportsLoad

30

33% Implementation Challenges - Transmission

Long lead time to build transmission Uncertain which generation projects will developUncertain where transmission upgrades will be neededCPUC study shows need for up to 7 new major high voltage transmission projectsIndicative Nexant results

31

Nexant Results – Transmission Expansion Bulk Transmission Upgrades For 33% RPS – 2020

32

33% Implementation Challenges - Transmission

Potential Source of Multi-Year Delay for HV TransmissionTypical lead-time 6-11 yearsTypical process involves:– Project study and approval by CAISO (1-2 years)– CPUC approval (2-3 years)

• Can add significant delays, e.g., delay in approval of Sunrise Project• Other litigation post CPUC approval can also delay the project

– Engineering/Procurement (1-3 years)– Construction/Environmental Mitigation (2-3) years

• Delays could be based on the route and degree of environmental mitigation

33

33% Implementation Challenges - Resources

Need for non-renewables dependent upon renewable mixFor example a high wind case would require more “capacity” to meet Planning Margin than a high solar– 3000 MW needed for Wind (10,000 MW) and Solar (2,000 MW)– 300 MW needed for Wind (4000 MW) and Solar (8000 MW)– Assuming wind at 15% and solar at 60% capacity credit

Regulation and ramping needs are dependent upon renewable mix– Not well understood at this time for full mix of renewables– CAISO 33% RPS Integration studies underway to clarify

requirements

34

What are the opportunities to address these challenges

Focused geographical development to streamline transmission siting to reduce the time to build transmissionImproved longer term analytical tools to help narrow the uncertainty as 2020 approaches - more probabilisticPRM may have to be expanded to include integration requirementsPotential increased use of distributed generation to reduce the transmission delays associated with larger scale remote wind and solar

35

What are the opportunities to address these challenges

Improved control over new wind and solar to deal with severe ramps and over-gen eventsPotential increased role for storage and demand side responses to address regulation, ramps and over-genImproved wind forecasting to reduce daily uncertainty and thus reduce regulation and load following requirementsImproved solar generation forecasting through better cloud cover forecasting to reduce daily uncertainty

36

Questions

Questions?