environmental energy technologies fridayforum020531.ppt the end-use forecasting group: who we are...

Environmental Energy Technologiesfridayforum020531.ppt

The End-Use Forecasting Group: Who we are and what we do

Jonathan G. [email protected], 510/486-5974, http://enduse.lbl.gov/

Talk is on the web at

http://enduse.lbl.gov/shareddata/fridayforum020531.ppt

Friday Forum

Lawrence Berkeley National Laboratory

May 31, 2002


Who we are LBNL’s End-Use Forecasting (EUF) Group established

in 1991. Core team includes Rich Brown, Bill Golove, Etan

Gumerman, Greg Homan, Jon Koomey, Kathryn McCarthy, Marla McWhinney, Mithra Moezzi, Maggie Pinckard, Judy Roberson, Carla Rose-Holman, Alan Sanstad, Osman Sezgen, Carrie Webber, Tom Wenzel

Staff from other groups work with us regularly: Bart Davis, Karen Herter, Alan Meier, Evan Mills, Bruce Nordman, Jeff Warner

Funding almost exclusively from EPA.


Selected projects 5-lab (1997) and Clean Energy Futures Studies (mid-Nov 2000) Tax credits analysis - Climate Change Technology Initiative (1997) Energy Star technical support for program decisions (includes work

on new products) Energy Star (E*) impacts calculations for CCAP Scenario analysis tools (e.g. NEMS, BEAST, other spreadsheet tools) Information technology and resource use Data collection/measurements for E* office equipment, consumer

electronics, and other products (ongoing) Home Energy Advisor/Home Energy Saver Peak demand/screening curves Conservation supply curves Debunking of urban legend about office equipment electricity use Collecting measured data on server farm power use.


How do we continue to be effective? By thinking ahead: Understand EPA’s needs and be proactive

in meeting them. By relying on data: confront speculation with measurements,

avoid obsessions with models and computer tools. By being complete, accurate, and thorough: produce well-

documented and well-constructed analysis focused on real decisions.

By being fast: get a credible answer in the time allotted By being translators: draw on detailed technical work from

other research (e.g. appliance standards analysis) By being recognized: publish in peer-reviewed journals. By being interdisciplinary: (fields include engineering,

economics, statistics, architecture, energy and resources, and others).


Home Energy Saver/Advisor Siteshttp://hit.lbl.gov and http://hes.lbl.gov


Standby power for TVs

0%

5%

10%

15%

20%

Standby Power (W)

0 5 10 15 25

Energy Star Limit(3 Watts)

33% 67%

N=365

20

Source: Karen Rosen, LBNL, May 1999, [email protected]

Sh

are

of

un

its

mea

sure

d


How do tax credits work?

Source: LBNL analysis of administration’s CCTI tax credits, memo dated 13 Feb 1998. http://enduse.lbl.gov/Projects/TaxCredits.html

Cumulative effect (2000-2015) of proposed CCTI tax credits on adoption of more efficient technologies

31%

11%

30%

24%

39%

65%

0%

20%

40%

60%

80%

100%

CACs HPWHs

Cost reductions from increased

production experience

Announcement effect

Direct price effect


Scenarios of U.S. Carbon ReductionsPotential Carbon Savings from High-Efficiency Low Carbon Case in 2010


Market Imperfections: Efficient Magnetic Ballast Market Shares

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

CA stds

NY stds

MA/CT stds

FL stds

US stds

Major ballastmanufacturer's estimate

of share in 1980Major ballast

manufacturer's estimateof share in 1988


CA Households per MW of Capacity

0

500

1000

1500

LADWP PG&E SCE SDG&E SMUD StatewideTotal

Number of households per average MW

Number of households per peak MW

households/MW

Source: CEC; 1999 data


Electricity used by the Internet

0

50

100

150

200

250

300

350

Forbes article (Mills 1999) Koomey et al. 1999

Energy to manufacture equipment

Routers in LANS and WANs

Routers on Internet

PCs at home for all purposes

PCs in offices for all purposes

Telephone central offices

Web sites

Major dot-com companies

TWh per year

Source: LBNL-44698


local distribution lines

to the building, 480 V

HVAC system

lights, office space, etc.

UPS PDU computer racks

backup diesel generators

Electricity Flows in Data CentersElectricity Flows in Data Centers

computerequipment

uninterruptible

load

UPS = Uninterruptible Power Supply

PDU = Power Distribution Unit;


Additional research areas Tom Wenzel--analysis of automobile

emissions testing Bill Golove--Technical support to

clean energy projects Alan Sanstad--Forecasting and

divisia analysis


Lessons learned from the evaluation of vehicle inspection

and maintenance programs

Tom WenzelFriday Forum


May 31, 2002


Evaluation of I/M Programs Use multiple data sources to evaluate programs

— test result records— roadside remote sensing emissions measurements— vehicle registration data

Analyses of CA and AZ programs

Findings incorporated in— report of NRC panel on vehicle emissions modeling— report of NRC panel on I/M program evaluation— forthcoming EPA guidance to states on program

evaluation

Find out more at:— http://eetd.lbl.gov/LabOnlyWS/Intranet/Archives/

DivRev02/wenzel.pdf


Theoretical I/M Program

0 1 2 3 4

Year

Avera

ge E

mis

sions

of

Giv

en F

leet

emissions increase without I/M

repairemissions

increase after I/M


Lessons Learned about Actual I/M Programs

0 1 2 3 4

Year

Avera

ge E

mis

sions

of

a G

iven F

leet

emissions increase without I/M

repair

emissions increase after I/M

actual emissions increase after I/M


Technical Support to Clean Energy Projects

William GoloveFriday Forum


May 31, 2002


Project Areas USPS

— Building energy consumption managements Shared Energy Savings (SES) contract (1600 bldgs)s CEC demand response (24 plants)s Consumption tracking and goals (2000+ bldgs, 10

districts)— On-site generation

s Marina PV (largest federal building intergrated system)s San Bernardino natural gass Chiquita Canyon LFG to electricity

— Renewables s Largest federal direct access purchase (4.7MW)

USDOE— Assistance to federal agencies (Air Force) in purchasing

renewables— Assistance to Public Renewables Partnership (PRP)

USAID— ProForm


Chiquita Canyon Landfill Gas project (2MW)

USPS received offer of $0.14/kWh for 10 year firm delivery of electricity from LFG

Requested assistance with evaluation and negotiations from LBNL

Initial analysis looked at 15 yr project because of tax depreciation/residual value issue; completing 10 yr analysis

Found electricity prices should range between 5.0 to 7.1 cents/kWh at 20% after tax return on equity

Substantial additional cost uncertainties exist, including: exit fees, standby charges (energy and capacity), ancillary services and grid management fees, and historic procurement charges (total 3 – 8 cents additional)


Retrospective on long-term energy forecasts, and divisia

decomposition of recent trends

Alan SanstadFriday Forum


May 31, 2002


Retrospective evaluation of long-range energy projections

(Sanstad, Laitner and Koomey 2001)

How well have energy models performed?

We examined five studies conducted in 1982-3, focusing on projections (U.S.) to year 2000

Characteristic pattern: reasonably accurate demand forecasts but dramatic over-estimation of energy prices


U.S. energy demand, 1982-2000:Five projections, and actual

(Median year 2000 error: -5.2%)

70

80

90

100

NEPP 83AGAGRIDRIAESActual


World oil price, 1982-2000:Five projections, and actual

(Median year 2000 error: +197%)

0

10

20

30

40

50

60

70

80

90

100

NEPP 83AGAGRIDRIAESActual


Estimated GDP losses from 15% energy tax in year 2000:

Median and perfect hindsight model predictions

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0.1 0.2 0.3 0.4 0.5 0.6 0.7

Substitution elasticity

Median model

Perfect hindsight


An index analysis of recent changes(Davis, Sanstad, Koomey 2001)

Focus: post-1996 acceleration of E/GDP and C/GDP declines.

The EIA says: "It was the weather."

Our approach to testing this: Weather-corrected Divisia index decomposition of changes in primary fossil energy use-to-GDP ratio

Conclusion: Weather accounts for about one-half the acceleration


Fuel mix and weather effects on energy and carbon intensity

B B BB B

BB

B BB B B

B B B B B B

JJ

J J

J

JJ J

J JJ

J JJ

JJ

JJ

H H

H H

H

HH

H

H

H H

HH

H

H

H

H

H

F F

F

F

F

F

F

F

F

FF

FF

F

F

F

F

F

96Q1 96Q2 96Q3 96Q4 97Q1 97Q2 97Q3 97Q4 98Q1 98Q2 98Q3 98Q4 99Q1 99Q2 99Q3 99Q4 00Q1 00Q2

0.85

0.9

0.95

1

1.05

Fuel Mix

Weather

E / GDP

C / GDP

Fuel Mix Effect

Fuel Mix Effect

Weather Effect

Effects unrelated

to Fuel Mix or

Weather

environmental energy technologies fridayforum020531.ppt the end-use forecasting group: who we are...

Documents

energy star technical

new products energy

tax credits work

wellconstructed analysis

ccap scenario analysis

measured data

appliance standards

detailed technical work