# Presentation: 247957

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

- Jonathan G. Koomey
- JGKoomey@lbl.gov, 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 Sites

- http://hit.lbl.gov and http://hes.lbl.gov

## Standby power for TVs

- Source:  Karen Rosen, LBNL, May 1999, KBRosen@lbl.gov

**Share of units measured**

## 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

## Scenarios of U.S. Carbon Reductions

- Potential Carbon Savings from High-Efficiency Low Carbon Case in 2010

## Market Imperfections: Efficient Magnetic Ballast Market Shares

## CA Households per MW of Capacity

- Source:  CEC; 1999 data

**Notes:**
The range of demand is 900-1400 houses/MW on average
300-800 houses/MW on peak.
SMUD has lower values because the climate is hot and has more SF houses.

## Electricity used by the Internet

- Source:  LBNL-44698

## Electricity Flows in Data Centers

- 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 Centers**

- computer
- equipment

- 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 Wenzel
- Friday Forum
- Lawrence Berkeley National Laboratory
- 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

## Slide 16

**Notes:**
Inspection and maintenance program involves bringing vehicle in for an emissions test every one or two years.  Vehicles that fail are supposed to be repaired until they pass a retest.  

This figure shows how the theoretical I/M program is supposed to work.  
Emissions of a fleet of vehicles will increase over time as they age, and components break on some of them.  The I/M program is supposed to identify broken vehicles and repair them to reduce their emissions.  Over time emissions increase again.  But the emissions are lower than they would have been without the I/M program.  Hopefully, over many I/M cycles emissions are stabilized, or reduced.  The area between the two curves is the benefit of the I/M program.

## Slide 17

**Notes:**
Our research has redrawn the theoretical picture of I/M.
We use two types of data to better understand how I/M programs work: I/M test records, and remote sensing emissions measurements of many vehicles as they drive on road.
The figure shows the lessons we’ve learned about I/M.  First, we’ve used test records over several cycles to more accurately draw the I/M sawtooth.  Although initial emission reductions are large, emissions deterioration between I/M cycles is also large, because many vehicles that fail in the first cycle (and are repaired to pass) fail again in the second cycle.
Using remote sensing measurements, we’ve learned that many motorists reduce their vehicle emissions in the few weeks before their scheduled I/M test.  These reductions are an additional benefit of I/M programs that are not measured when using test records alone.  
Using a subset of vehicles that get tested more frequently, we’ve found that many vehicles would have failed soon after passing their I/M test.  This means that the initial reduction from I/M is less than originally thought. 
Most recently, we’ve used initial I/M tests of vehicles upon their initial registration in an I/M area to estimate what emissions would have been without the I/M program.  This allows us to estimate the cumulative effect of I/M over several years. 

## Technical Support to Clean Energy Projects

- William Golove
- Friday Forum
- Lawrence Berkeley National Laboratory
- May 31, 2002

## Project Areas

- USPS
- Building energy consumption management
- Shared Energy Savings (SES) contract (1600 bldgs)
- CEC demand response (24 plants)
- Consumption tracking and goals (2000+ bldgs, 10 districts)
- On-site generation
- Marina PV (largest federal building intergrated system)
- San Bernardino natural gas
- Chiquita Canyon LFG to electricity
- Renewables
- 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 Sanstad
- Friday Forum
- Lawrence Berkeley National Laboratory
- 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%)

## World oil price, 1982-2000:Five projections, and actual(Median year 2000 error: +197%)

## Estimated GDP losses from 15% energy tax in year 2000:Median and perfect hindsight model predictions

## 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