SEDS Review

Chris MarnayMichael Stadler Inês Lima AzevedoJudy Lai Ryoichi KomiyamaSam BorgesonBrian Coffey

May 7, 2009

SEDS Building Sector Module

LAWRENCE BERKELEY NATIONAL LABORATORY is a U.S. Department of Energy National Laboratory Operated by the University of California

Building Sector in the SEDS Context

Macroeconomics

Biomass

Coal

Natural Gas

Oil

Biofuels

Hydrogen

Liquid Fuels

Heavy Transportation

Industry

Light Vehicles

Macroeconomics Converted EnergyPrimary Energy End-Use

Buildings

Electricity

3

Importance of the Building Sector

Large share of energy demand Building sector accounts for: 73% of electricity demand (2007), 33% of gas demand (2007), 38% of CO2 emissions (2006)

Has been fastest growing Electricity demand growth from 2000 to 2007 Building: +383 TWh, Industry: -58 TWh, Transport: +2 TWh (+2.2%) (-0.8%) (+5.3%)

Slow stock turnover (about 80 years)

Active, passive technologies & on-site generation (ZNEB)

Building Sector Data Flow

Macroeconomics

Natural Gas

Building

Liquid Fuels

Liquids Fuel

Electricity

Natural Gas

Light Fuel Oil Price

Natural Gas Price

Electricity Price

GDPMacroeconomic

sCapital Investments

Natural Gas Demand

Fuel Oil Demand

CO2 Emissions

Incoming Data Outgoing Data

Population

Disposal Income

Peak Electricity Demand

Base Electricity Demand

ElectricityElectricity Demand

5

Preliminary Assumptions

One national region Uniform energy price, uniform solar isolation… Space heating & cooling are considered by Census region

Two building types (commercial & residential)

Annual decision making Fixed market share allocation: α constant over the forecast period (except for PV)

6

Buildings Lite Module

Covers both residential and commercial

Tracks building stock

Meets building services

Treats buildings systemically

Enables analysis of major technologies R&D programs

Uses expert elicitation of potential advances

Runs stand-alone or integrated

7

Buildings Module Logic Flowinputs

(GDP, population, fuel prices etc.)

floorspace forecast

service demand forecast(not energy!)

passivehigh, medium and low

efficiency buildings

on-site generation (PV)

outputs(electricity, gas, light fuel demand,CO2 emissions, PV generation)

heatingcooling

lighting, DHW, refrigeration,ventilation, etc

active

- Heating insulation- Solar gains- Daylighting- Natural Ventilation

- HDD, CDD- Lighting- Hot water- Refrigeration- Ventilation- Plug loads

R&D is considered in lighting

Policy instruments

R&D is considered in PV

8

Early R&D Effect Examples

first cut photovoltaic example:

Takes stochastic inputs for GDP, energy prices, & population

Applies PV performance forecast

Implements expert elicitation of potential advances

Employs the systemic approach(other early example is solid state lighting)

9

Data Sources

Historic floorspace input data are based on commercial and residential energy intensity indicators based on Pacific Northwest National Laboratory(PNNL), Energy End-Use Flow Maps for the Buildings Sector, D.B. Belzer, September 2006

The final energy demand data, obtained from PNNL, CBECS, & RECS, as well as the Annual Energy Review (AER) for 2005, for each fuel were divided by the floorspace estimates for 2005 and used for service demand forecasting.

Equipment types were considered for refrigeration, space cooling, space heating, lighting, water heating, and ventilation. All other end-uses were categorized as plug loads. The installed equipment stock information is based on Berkeley Lab’s own calculations derived from appliance manufacturers’ shipments data, CBECS, RECS, McGraw-Hill Corporation’s Analytics and AEO-07 (Annual Energy Outlook 2007)

10

PV Module Modeling Framework Regression approach : PV generation is predicted by regression

- including PV cost as independent variable- not including PV cost as independent variable

Logit function approach - two kinds of α are assumed. with time lag : α is regressed with time lag of alpha and electricity price without time lag : α is regressed with electricity price - initial α determined by maximum likelihood

Electricity demand

Logitfunction

Electricity from utility

Onsite PV generation

PV cost,electricity price

Maximum PV penetration constraint:

- Peak load constraint- Rooftop constraint

α- With time lag- Without time lag

Electricity demand

Logitfunction

Electricity from utility

Onsite PV generation

PV cost,electricity price

Maximum PV penetration constraint:

- Peak load constraint- Rooftop constraint

α- With time lag- Without time lag

11

Expert Forecasts: PV in Commercial Sector

0

2000

4000

6000

8000

0% 20% 40% 60% 80% 100%

Percentile

PV System Cost (US$/kW)

2007

2010(No DOE Fund)

2010(DOE Program)

2020(No DOE Fund)2020(DOE Program)

0.10

0.17

0.24

0% 20% 40% 60% 80% 100%

Percentile

Module Conversion Efficiency (%)

20072010(No DOE Fund)

2010(DOE Program)

2020(No DOE Fund)

2020(DOE Program)

0

10

20

30

40

50

60

70

80

90

0% 20% 40% 60% 80% 100%

Percentile

PV O&M Cost (US$/kW)

2007

2010(No DOE Fund)

2010(DOE Program)

2020(No DOE Fund)

2020(DOE Program)

0

100

200

300

400

500

600

700

800

0% 20% 40% 60% 80% 100%

Percentile

Inverter Replacement Cost (US$/kW)

2007

2010(No DOE Fund)

2010(DOE Program)

2020(No DOE Fund)2020(DOE Program)

Effect on Demand (Commercial)

Total commercial sector electricity demand

Commercial PV generation

(No DOE Funding)

*Logit function approach (with time lag of alpha)*Results are simulated by building module, stand-alone

0

50

100

150

200

250

20

05

20

10

20

20

20

30

20

40

20

50

TWh / year

50th25th10th

75th

90th

percentile

1000

1200

1400

1600

1800

2000

2200

2400

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

TWh

Electricity Demand (Without PV)

PV (No DOE funding)

PV (With DOE funding)

0.E+00

1.E-11

2.E-11

3.E-11

0 100 200PV Generation in 2050

(TWh)

Probability

PV Generation of Commercial Sector in 2050

*Above results are simulated by integrated module (version R170)

0

500

1000

1500

2000

2500

Base Case High Oil Price

High Gas Price

Carbon Cap

DOE R&D Subsidy 50%

Electricity from utility

PV

TWh

Net Energy Demand of Building Sector

Total Net Energy Demand(Light fuel, Gas and Electricity)

Carbon Cap - Base Case in 2050

*Above results are simulated by integrated module (version R170) 14

0

1000

2000

3000

4000

5000

6000

7000

8000

20

05

20

10

20

20

20

30

20

40

20

50

TWh

Carbon Cap

Base Case

-300

-250

-200

-150

-100

-50

0 TWh

CO2 Emissions of Building Sector

CO2 EmissionsCarbon Cap - Base Case

in 2050

*Above results are simulated by integrated module (version R170) 15

-1400

-1200

-1000

-800

-600

-400

-200

0 Mt-CO2

0

500

1000

1500

2000

2500

3000

3500

20

05

20

10

20

20

20

30

20

40

20

50

Mt-CO2

Carbon Cap

Base Case

16

Future Work

*Regionality

Other passive & active technologies *Windows, heat-pump water heaters, geothermal heat pump, & ……..

Building types

Other on-site generation technologies; CHP …

Logit sophistication

ON TO THE NEXT MODULE

Comparison of PV in Building Sector* with AEO209

* Commercial + Residential, using integrated version R.170

PV Capacity PV Capacity & Generation

**AEO2009: Annual Energy Outlook 2009 with Projections to 2030, Updated Annual Energy Outlook 2009 Reference Case

with ARRA

0

50

100

150

200

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

GW

AEO2009

DOE R&D

Base case

0

50

100

150

200

0

50

100

150

20

05

20

10

20

15

20

20

20

25

20

30

20

35

20

40

20

45

20

50

GW

DOE R&D(GW)

DOE R&D(TWh)

TWh

AEO2009(TWh)AEO2009(GW)

Base(TWh)Base(GW)

Sensitivity Analysis

Base case: No carbon regulation, no forcing of prices, no DOE funding, R&D improvements

High oil price scenario: $100/bbl in 2005, ramps linearly to $500/bbl by 2030, and stays at $500/bbl for rest

of simulation

High natural gas price: $8/MMBtu in 2005, ramps linearly to $50/MMBtu by 2030, and stays at $50/MMBtu

for rest of simulation

Carbon cap: Starts at 5902 million metric tCO2/yr in 2010, ramps linearly down to 4000 million

metric tCO2/yr by 2035 (roughly 80% of 1990 levels), and stays at 4000 million metric tCO2/yr for rest of simulation.

DOE R&D program: Implement improvements associated with target-level funding for technologies that

have program goals.

Subsidy 50%: 50% of PV cost is subsidized.

20

)*exp( ,, titi LCOE

iti

titiMS

,

,,

MS = Market shareLCOE = levelized cost of energy (>0)υ = utilityα = scaling factori = technology types 　　 i {utility electricity, PV gen.}∈t = time

10

15

20

25

30

35

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

0.00.10.20.30.40.50.60.70.80.91.0

0.60

0.54

0.48

0.42

0.36

0.30

0.24

0.18

0.12

0.06

PV generation cost ($/ kWh)

Shareα = 10

α = 5

α = 3

α = 1

α = 0

Electricity price= 0.3 ($/ kWh)

priceyelectricitBA *

Role of Logit Alpha

21

PV Generation in Building Sector(Without DOE funding)

0

100

200

300

400

500

600

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

TWh

AEO2009

"Without Time Lag"

"With Time Lag"

*Results are simulated by building module, stand-alone version 04/09/09

22

Maximum PV Generation based on the Peak Load Constraint and PV generation in the Building Sector

(Without DOE Funding)

0

100

200

300

400

500

600

2005

2009

2013

2017

2021

2025

2029

2033

2037

2041

2045

2049

TWh

Maximum PV generation based on thePeak Load Constraint

PV generation(Without Time Lag) PV generation

(With Time Lag)

*Results are simulated by building module, stand-alone version 04/09/09

23

PV Generation in the Building Sector under DOE Funding Scenario

(Alpha is Subject to “With Time Lag”)

0

100

200

300

400

500

600

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

TWh

AEO2009"Without DOEFunding"

Maximum PV Generation

"With DOEFunding"

*Results are simulated by building module, stand-alone version 04/09/09

24

From DOE projections!

2007

2010 no DOE

2015 no DOE

2020 no DOE

2020 with DOE funding

DOE projection for LED device cost in 2015 (with funding)

DOE projection for device cost in 2020 (with funding)

$/klumen

2010 with DOE funding

2015 with DOE funding

percentile

S.S. Lighting Example

25

SSL Efficacy

2020 with DOE funding

DOE’s 2007 estimate for LED luminaire efficacy

DOE’s 2012 projection for luminaire efficacy (with funding)2020 no DOE funding

DOE’s 2010 projection for LED luminaire efficacy

DOE’s 2015 projection for luminaire efficacy (with funding)

2626

Lighting Consumption (Commercial)

300

400

500

20

05

20

10

20

15

20

20

20

25

20

30

20

35

20

40

20

45

20

50

Lighting electricity demand (TWh)

50th percentilew ithout DOE R&D

95th percentilew ithout DOE R&D

5th percentilew ithout DOE R&D

50th percentilew ith DOE R&D

95th percentilew ith DOE R&D

5th percentilew ith DOE R&D

*Results are simulated by building module, stand-alone version

PV Generation of Building Sector in 2050

*Above results are simulated by integrated module (version R170)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Base Case High Oil Price

High Gas Price

Carbon Cap

DOE R&D Subsidy 50%

Electricity from utility

PV

TWh

28

Buildings Module Logic Flowinputs

(GDP, population, fuel prices etc.)

floorspace forecast

service demand forecast(not energy!)

passivehigh, medium and low

efficiency buildings

on-site generation (PV)

outputs(electricity, gas, light fuel demand,CO2 emissions, PV generation)

heatingcooling

lighting, DHW, refrigeration,ventilation, etc

active

- Heating insulation- Solar gains- Daylighting- Natural Ventilation

- HDD, CDD- Lighting- Hot water- Refrigeration- Ventilation- Plug loads

R&D is considered in lighting

Policy instruments

R&D is considered in PV