1. A Two-Level Electricity Demand Model Hausman, Kinnucan, and Mcfadden

2. Introduction Analyzed the Connecticut Peak Load Pricing Test – Oct 1975 to Oct 1976 – 199 Households – Meters installed to measure electricity consumption – Faced a peak load pricing system with 3 prices 16 cents / kwh peak, 3 cents / kwh intermediate, 1 cent / kwh off-peak.

3. Procedure Determine household preference for amount to spend on electricity Estimate the cost of that electricity based on time of consumption (TOD pricing) or previous rate structure (controls) Determine from relative demand and pricing, the total demand

4. Technique Estimate consumer demand w/ two stage budget – Treat electricity demand within each period as a separate commodity Estimate demand across periods conditioned on relative prices, appliance stock, household characteristics, and environmental effects

5. Technique Estimate a price index for electricity – Household consumption determined by price of electricity and prices for other goods – Absolute demand is derived from relative demand and price index

6. Two Level Budgeting Model imposes seperability between consumption of electricity and other commodities – Decides how much to spend on electricity and all other goods

7. Derivation of Time of Day Model Distribution of relative load across households more stable than consumption levels – Two level budgeting model Reduces complication of controls (due to declining block tariff) Requires linear homogeneity Considers household electricity consumption in a representative day. – Appliance mix is assumed exogenous, therefore no effect on consumption patterns – Seasonality ignored – Interday effects ignored – Rates do not change with consumption only time of day.

8. Derivation of time of day model Assume: – Utility determined by vector of electricity consumption at different times of day, and consumption of all other goods (x0) – HH decides how much to spend on electricity and how much to spend on other commodities – Total consumption then depends on price of electricity

9. Derivation of Time of Day Model Divide day into series of 15 minute periods – Vector of electricity rates with a price corresponding to each period – Indirect utility function: – Utility function from with assumptions: – Utility function for electricity Where, total electricity expendature

10. Derivation Indirect utility function Use Roy’s identity, linear homogeneity and Eulers theorem to get demand function

11. Derivation Proportion of total consumption in base period Consumption in any period relative to base period is

12. Derivation Taylor expansion gives Full accounting gives single period cons. func

13. Derivation Price index to use in consumption function determined by total electricity consumption and total consumption.

14. Derivation Used to derive demand function for total daily demand: Pre-experimental period, price constant on all periods, but is a function of monthly demand due to declining block rate structure.

15. Estimation Sample of 150 households used – 20 not used to estimate, but used to test Two levels of demand estimated – Estimated with daily data using demand equation – Estimated price index (post exp) or insturmental variables (pre exp) used to estimate consumption equation

16. Problems Not a random sample – Chosen based on an endogenous variable Residuals surely correlated with RHS vars Inverse sample weights (WLS) used to correct for this issue – Estimates not efficient – Downward biased standard errors – Voluntary participation Unobserved attributes determine participation, cannot be checked Aigner and Hausman (1978) find that elasticity lower when not subject to voluntary choice, thus here, elasticity may be overstated.

17. Estimation First level – Hourly demand equations – Estimated from 4 periods, weekdays and weekends during 2 winter months and 2 summer months, corresponding to system peak demand periods. – 11pm – 7am is base period

18. Results Reported results for 3 representative periods, peak, off peak and intermediate Coefficients from demand equation are all relative to base periods – Not accounting for long run substitution

20. Findings Appliance coefficients have correct sign – Range / clothes dryer should be positive, since they will be operated during the day Range use lower when facing peak pricing, moved to intermediate period – Freezer should have negative coefficient since it operates all the time, this increases the base period demand for the household. Heater has spillover effects – Households used less heat energy during peak periods, lower demand spills over to non peak periods because people don’t readjust the thermostat

21. Findings Elasticities are negative – 9-11 am more elastic than 5-7 pm – Intermediate period elasticities are lowest near peak periods – Day of the week variables suggest demand changes occur more during the day and less between days First state equation is reasonable

22. Price index estimate Compared pre experimental rates to post experimental price index – Treat declining block rate structure as linear two part tariff Lump sum $6.46, marginal price is 3.54 cents / kwh – Experimental period prices come from price index equation, using estimated coefficients from demand equation estimate Winter weekdays: 3.89 cents / kwh High: 4.66 cents/kwh; Low: 3.25 cents/kwh

23. Price Index Estimate Comparing experimental period to pre experimental period: – Households faced higher marginal cost by ~16% but no lump sum payment during experiment – Decrease in consumption did occur 1% from previous year and 5% decline from controls – Increase in marginal prices outweighs the removal of lump sum payment

24. Estimation Second Level – Daily demand equations – 4 demand estimates Weekday / weekend, winter / summer – Less precise than first level estimates – Coefficients generally have correct total effect Presence of electric heat adds consumption during winter 1% rise in MP leads to reduction of weekday consumption by ~4.3% Freezer adds to consumption, but elasticity is not significant, indicating non-discretionary nature of this appliance

26. Validation Used 20 households from original sample to test predictive capacity of model Forecasts do well in peak pricing periods forecasting relative load, though not significant Large forecast errors for other periods, insignificant results, less important

27. Validation When aggregated by period, forecasts appear accurate – Peak usage in validation group occurs in non-peak pricing period. Model correctly predicts this

28. Validation Next, use forecasted price indices to forecast demand – Over prediction for low demand customers – Under prediction for high demand customers – Even when authors correct for sample issues, results are not significant – Second level (daily demand) not as reliable as first level (hourly demand) equations

30. Validation Load forecasts better in peak than non peak periods – Smaller errors in peak periods forecast load reasonably well