Anant Sudarshan anants@stanford.edu Dr. James Sweeney
1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 KWh per capita 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 Residential California Commercial California Industrial California Residential US Industrial US Commercial US Data shows retail electricity sales over three economy sectors Source: California Energy Commission and Energy Information Administration
Wyoming Kentucky District of Columbia Alabama South Carolina Tennessee Louisiana North Dakota Indiana Arkansas West Virginia Mississippi Nebraska Idaho Oklahoma North Carolina Texas Georgia Montana Delaware Iowa Virginia Kansas Ohio Missouri Nevada Washington Minnesota Oregon Wisconsin Florida South Dakota Maryland Pennsylvania Arizona Illinois Michigan New Mexico Colorado Utah Connecticut Vermont New Jersey Maine Alaska Massachusetts New Hampshire Hawaii New York Rhode Island California KWh per person 16000 14000 12000 10000 8000 6000 4000 2000 0 Per Capita Electricity Consumption (2005) World High Income Middle Income Low Income United States 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 State Electricity Consumption (million KWh)
Context Methodology and sector details Results Conclusions Concerns and next steps
Is this the best representation to use? What would this comparison look like if California had the same characteristics as the US as a whole? How much has demographics, climate, industry structure contributed to this picture? What role has policy played here? Is there empirical data out there that can help answer some of these questions? How can we best use the limited data that exists?
Identify multiple influencing factors that could cause reductions in observed electricity consumption Show that statistically significant effects are associated with these factors Use the limited empirical data available to arrive at first cut quantifications Compare this estimate with CEC model projections
Identify basic consuming unit: e.g. Households Identify an influencing factor: e.g. Income Obtain state and national level distributions : % of HH in different income groups Obtain national level data on the variation of electricity use as a function of the chosen factor: KWh/HH = f(income Group) Substitute CA household income for US household income, all else unchanged.
1. Household income 2. Householder age 3. Household size 4. Climate characteristics Population weighted CDD, HDD 5. Urbanization 6. Housing unit floor space 7. Housing unit age 8. Fuel choices (space heating and water heating) 9. Appliance use patterns ( lifestyles ) Basic unit of consumption: Household
Air Conditioning ~ cool cac cac CDDca E ca Eus CDD α i ca CDD i E i us E cool us rac ca E rac us CDD CDD Fraction of households using central (i=cac) or room (i=rac) air-conditioning Population weighted cooling degree days (i = CA or US) National average electricity consumption for air-conditioning (KWh/HH) by households using equipment type cac or rac respectively (i=cac/rac) Estimate of per household air-conditioning electricity consumption for an average US household transplanted to a CA climate. ca us E us Empirically estimated national average household electricity consumption for air-conditioning Test Hypothesis: ~ E cool Eˆ us
H i ca E i us T ca Household Size E si us i i HcaE T California households in size group i National average household energy consumption in size group i Total number of households in California ca i us α us E si E us Electricity as a fraction of household energy (US) Estimate of per household air-conditioning electricity consumption for an average US household corrected to have CA household sizes Empirically estimated national average household electricity consumption Test Hypothesis: E si Eˆ us
1. Industry profile Nature of manufacturing units 2. Number of employees by NAICS code (industry type) 3. Distribution by unit size within each industry type 4. On-site electricity generation 5. Process differences reflected in fuel mix differences
E ind i j p P ij i ij E ind α ij η i p ij P Estimated industrial electricity use per capita for CA, correcting US figures for industry structure energy use per employee in industry type i belonging to size group j (US av.) electricity as a percentage of total energy consumption in industry I (US av.) employees in industry type i of size group j (CA) Total state population (CA) National on-site generation in 2002: 152600 KWh (15.54 percent of industrial sales) State on-site generation in 2002: 17142 KWh (35.45 percent of industrial sales)
1. Sector profile Nature of commercial services 2. Percentage of total building floor space used for different services 3. Floor space per capita for different services (Building intensity ) E com f P Basic unit of consumption: Buildings (Floor Space) i i i Where, E com = Estimated commercial sector KWh per capita for CA, correcting US figures for floorspace differences α i = US average KWh per square foot for service type i f i = floor space occupied by service type i in CA P = total state population
Context Motivation and objectives Methodology and sector details Conclusions Concerns and next steps
KWh per capita 6000 5600 5200 4800 Unexplained Commercial 272 Unexplained Industrial 416 4400 4000 Unexplained Residential, 545 Self Generation, 258 3600 3200 2800 Industry Composition 1325 2400 2000 1600 1200 800 400 0 Cooling Load, 332 Heating Load, 340 Household Size, 382 Water Heating, 238 Commercial Floorspace 1036 Urbanization, 321 Household Income, -130-400 -400
California Energy Commission estimates savings that are reasonably expected to occur Savings figures from overall energy model Individual econometric models for some programs Utility estimates for their programs
KWh per capita 1400 1200 Unexplained Industry Unexplained Commercial Unexplained Residential CEC Efficiency Estimates 1000 Unexplained Industry 800 600 Unexplained Commercial CEC Efficiency Estimates 400 200 Unexplained Residential 0 1980 1990 2001-200
Can we obtain comparisons for past years? Can we explain why differences were so much smaller a few decades ago? 3.2 3 2.8 2.6 2.4 2.2 2 Household Sizes in the US and CA 1970 1980 1990 2000 US CA 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% California Electric A/C Saturation 1980 1990 2001
KWh per capita 5000 4500 4000 Unexplained, 545 3500 3000 Unexplained, 117 Urban Rural, 103 Water Heating, 239 Urban Rural, 321 Unexplained, 376 Urban Rural, 147 Size, 382 Size, 135 Water Heating, 269 Water Heating, 238 2500 Climate, 606 Climate, 545 Climate, 672 2000 1500 1000 California Actual 2116 California Actual 2281 California Actual 2225 500 0-500 Income, -41 Income, -118 Income, -130 1980 1990 2001
KWh per capita KWh per capita 4500 4000 3500 3000 2500 2000 1500 1000 500 0 1979 1983 1986 1989 1992 1995 1999 2003 Unexplained Reductions Reductions from Floorspace Differences CA Commercial Sector Electricity Consumption 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961 1960 Industrial California Industrial US
Context Motivation and objectives Methodology and sector details Results Concerns and next steps
Evidence to suggest a substantial role for policy (~23 percent of observed differences) Empirical evidence suggests that many factors influence electricity use. Can policy be made more innovative? Should energy efficiency policy have a broader ambit? Lifestyles may have a significant role to play Important to be aware of fuel mix differences and self generation differences (false economies?)
Context Motivation and objectives Methodology and sector details Results Conclusions
Endogeneity and dependence Difficult to correct without better data No data for states other than NY, TX, CA, FL Possible option: Multivariate model for household sector using 2005 RECS microdata Functional forms of dependence (Linearity not necessary) Better analysis of time periods before 2001 esp. for the industrial sector Spillover benefits ignored Extension of work From electricity to energy Include transport sector From California to the US From California to international Account for behavioral differences and the role of education and awareness programs.
Questions?
Percentage share Percentage share Decreasing energy use Increasing economic output 45 40 35 30 25 20 15 10 5 0 Daytime Unoccupied Temperature Profile 40 35 30 25 20 15 10 5 0 California United States Sleeping Temperature Profile California United States
19.00% Energy Use by Sector 18.00% 41.00% 22.00% Percentage Electricity Consumption 6.00% 9.00% 37.00% 16.00% 32.00% Transportation Industrial Commercial Residential Commercial Residential Industrial Mining and Agriculture Transport and Street Lighting Source: California Integrated Energy Policy Report (2007)
Schipper, L. & McMahon, J. E. (1995), 'Energy Efficiency in California: A Historical Analysis.'(ERCDC A.24 S336a 1995), Technical report, American Council for an Energy-Efficient Economy. Loughran, D. & Kulick, J. (2004), 'Demand Side Management and Energy Efficiency in the United States', Energy 25(1), 19-43. Bernstein, M.; Lempert, R.; Loughran, D. & Santana-Ortiz, D. (2000), 'The Public Benefit of California's Investments in Energy Efficiency'(MR-1212.0-CEC), Technical report, RAND. Auffhammer, M.; Blumstein, C. & Fowlie, M. (2007), 'Demand Side Management and Energy Efficiency Revisited'(CSEM WP 165r), Technical report, Center for the Study of Energy Markets: University of California, Multi-Campus Research Unit.
Roy, J.; Sanstad, A.; Sathaye, J. & Khaddaria, R. (2006), 'Substitution and price elasticity estimates using intercountry pooled data in a translog cost model.'(lbnl- 55306), Technical report, Lawrence Berkeley National Laboratories Report. Reiss, P. C. & White, M. W. (2005), 'Household electricity demand revisited', Review of Economic Studies 72, 853-883. Branch, E. (1993), 'Short run income elasticity of demand for residential electricity using consumer expenditure survey data.', Energy 14(4), 111-121.
Source: Presentation by Arthur Rosenfeld to the Power Association of Northern California (2006)
Source: 2006 CEC Demand Forecasts
Objectives Methods and Sector Details Results Conclusions Concerns, caveats and next steps