Price Elasticity of Demand in Current Zonal Market

1. Price Elasticity of Demand in Current Zonal Market PUCT Demand Response Workshop (Project No. 32853) Jay Zarnikau Frontier Associates January 2007

2. Research Questions • To what degree do industrial energy consumers respond to wholesale prices in the current zonal ERCOT market? • Can we quantify the “average” response? • Is the response to a likely 4-CP event similar to the response to a high balancing energy price?

3. Two Papers • Zarnikau, Jay, Greg Landreth, Ian Hallett, and Subal Kumbhakar, “Industrial Energy Consumer Response to Wholesale Prices in the Restructured Texas Electricity Market,” forthcoming in Energy – the international journal. • Zarnikau, Jay and Ian Hallett, “Aggregate Customer Response to Wholesale Prices in the Restructured ERCOT Market,” draft January 2007. (This one is still a work in progress.)

4. What We’ve Done • Analyzed 15-minute load data. • Statistically analyzed the how load changes in response to price signals (balancing energy and/or 4 CPs), after controlling for the effects of weather, daytype, and changes in natural gas prices.

5. What We’ve Done • In our first study: • We modeled the 20 largest industrial electricity consumers in the CenterPoint service area. • We do not know the actual identities of these consumers. • All data are for 2003. • In our second study: • We modeled the entire aggregated industrial load in ERCOT (all customers with IDRs). • Data are from January 2, 2002 to April 2005.

6. The Math. Yuck! • A Symmetric Generalized McFadden cost function:

7. The Math. It gets worse! • With demand functions:

8. In the first study • Data were aggregated into 3-hour blocks (which may cause some endogeneity concerns if demand response changes market prices within the 3-hour period). • Each of the 20 energy consumers were modeled separately, but the aggregated load of all 20 was also modeled. • It takes about an hour to estimate each model on a PC.

9. In the second study • 15-minute data were directly used. • Fourier series were used, so that the model could actually be solved. • 96 demand functions are estimated (one for each 15-minute interval in a day). • 21 MB of computer code, and another 20 MB of data. • A model run takes about 3 hours on a PC.

10. So, what did we find? • Just looking at the data suggests that some customers respond to balancing energy prices and 4 CPs. Others do not.

11. Elasticities • Keep in mind that a price elasticity of demand is the percentage change in demand associated with a 1% change in the price. • (% change in Quantity)/(% change in Price) • These are averages. • In this context: • Own-price elasticity is the change in demand in period t associated with a change in the price in period t • Cross-price elasticity is the change in demand in one period associated with the change in price in another period.

12. Price Elasticity of Demand Estimates for Aggregated Block of Industrial Load for Largest Industrial in Houston

13. Price Elasticity of Demand Estimates for Each of the 20 Industrials in Houston

14. Price Elasticity of Demand Estimates for Each of the 20 Industrials in Houston

15. Sanity Check on Results • Based on some simple comparisons of the aggregate load levels of transmission voltage (large industrial) energy consumers between days of likely 4 CP charges and adjacent days, the ERCOT staff has identified about 600 MW of aggregate demand response or about a 1% reduction in demand. • A “back of the envelope” calculation using the price increase associated with a 4 CP hour, a normal aggregate load level for these twenty energy consumers, and the estimated own-price elasticity for the 3 p.m. to 6 p.m. period would suggest a demand response of nearly 100 MW. • It seems plausible that these 20 industrials might account for about one-sixth of the total demand response in ERCOT to a 4 CP event.

16. What if we take out the 4 CPs? • In the results presented above, the price signal to the energy consumers contains two components: • the wholesale balancing energy price • and the transmission price (based on the 4 CP formula). • When the transmission price signals were removed from the price series, the own-price elasticities increased for the time periods of midnight to 3 a.m., 3 a.m. to 6 a.m., 6 p.m. to 9 p.m., and 9 p.m. to midnight, but were lower for the other (late morning and afternoon) hours.

17. Results for Aggregate Market-Wide Response of Industrials • Average own-price elasticity is -.00004. • Not surprisingly, the average market-wide estimates are much smaller than the elasticity estimates for the 20 largest industrials in the Houston area.

18. Results for Aggregate Market-Wide Response of Industrials Complete matrix of elasticities

19. Impediments to Price Response • Many large industrials are self-scheduled LaaRs, and are constrained in their ability to respond to prices. • Many energy consumers prefer a predictable flat price. • Advance notice of prices is limited. • 4 CPs cannot be predicted with perfect accuracy. • The $1000/MWh wholesale price cap was in place during the periods studied.

20. Implications • It might be difficult to rely upon price response to balance supply and demand in an energy-only market, unless more is done to facilitate demand response. • While it might be useful for ERCOT’s short-term load forecasts to take demand response into account when high prices are expected, the adjustments will likely to small.