Efficiency, Wealth Transfers and Risk Management Under Real-time Electricity Pricing

1. Efficiency, Wealth Transfers and Risk Management Under Real-time Electricity Pricing Severin Borenstein Haas School of Business, UC Berkeley University of California Energy Institute IDEI Economics of Electricity Markets Conference – June 2-3 2005 U.C. Energy Institute

2. The Simple Economics of RTP • Economists favor Real-Time Pricing (RTP) • RT Metering is not costly for large customers • RTP sends accurate signals to customers • Increased elasticity lessens market power • Political reality: retail markets will be, at best, a mix of customers on flat-rate service and RTP • Questions: • How Large are the Gains from RTP? • Who would be Winners and Losers? U.C. Energy Institute

3. Simulating A Long- Run Competitive Model of Electricity Markets • Demand differs in all hours • Free entry/exit of generation capacity in very small (1MW) increments • L-shaped production costs of each unit • 3 technologies – differ in FC and MC • Some customers on RTP, others on flat rate that covers its wholesale costs • all have same time-variation of demand U.C. Energy Institute

4. P6 D6 P5 P4 D5 P3 D4 P2 D3 P1 D2 D1 Kb Km Kp U.C. Energy Institute

5. Long-Run Equilibrium With RTP • For given capacities, Kb,Km,Kp, solve for SR competitive equilibrium • Then adjust capacities so that owners of each type of generation break even • Then adjust flat rate to retailer break even • This produces unique competitive equilibrium • Algorithm to find equilib starts with peaker capacity, then mid-merit, then baseload U.C. Energy Institute

6. Long-Run Equilibrium Without RTP • Find the flat rate that covers all costs when capacity is efficient for load • Equivalent to competitive wholesale price spike in peak hour equal to fixed costs of peaker • I assume that the demand distribution used (in this case from California ISO) results from combination of break-even flat rate and break-even time-of-use rate U.C. Energy Institute

7. What the model omits • Reserves • Plant outages -- increase price volatility • Market power of sellers • Risk-aversion of customers • Cross-elasticity of demand across hours U.C. Energy Institute

8. Data Inputs for Simulations • Demand profile: From CAISO for 1999-2003 (five years). Very similar results other systems • Demand elasticities: Broad range of estimates, most with large standard errors • use -0.025 to -0.500, constant elasticity demand • Price used include $40/MWh for T&D • Three production technologies based roughly on coal, combined-cycle gas turbine, and combustion turbine. U.C. Energy Institute

9. Table 1:Production Cost Assumptions U.C. Energy Institute

10. Basic Results – Capacity and Price Effects (table 2) • Large reduction in peaker capacity. Small changes in baseload and mid-merit capacity. • Very high peak prices with most inelastic demand, appx equal to capacity cost of peaker over sample • With a bit more elasticity (-0.1) peak prices below $10,000/MWh. • Still significant share of annual costs if not hedged U.C. Energy Institute

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12. Basic Results – Welfare Effects (table 3) • Total surplus increases with RTP, but at a decreasing rate as more move to RTP • 1/3 on RTP gives > ½ of total benefits • Both RTP and flat-rate customers benefit, but RTP customers benefit more • Flat-rate customers may not benefit (flat-rate may increase) if they have different load shape • TS gain as percentage of total energy bill is modest, but much larger than plausible cost of implementing RTP U.C. Energy Institute

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15. Results if elasticity varies with level of demand (tables 4 and 5) • Elasticity is linear function of (flat-rate) load, but weighted-average elasticity unchanged • Smallest elasticity is 50% original • BH show RTP could lower welfare if higher elasticity at peak demand time • But in simulations, benefits are greater with larger elasticity at peak • Larger effects on capacity, lower peak prices • Reduced effect if elasticity greater at off-peak U.C. Energy Institute

16. RTP vs Time-of-Use Pricing • TOU is just peak/shoulder/off-peak pricing • TOU captures <20% of realtime price variation • No obvious way to set TOU prices • Quasi-wholesale market with capital cost of peakers loaded onto period peak hour • Average-cost approach, spreads capital cost • Fixed ratio approach w/ ratio from actual TOUs • Regardless of TOU method, creates only 10%-20% of the gains from RTP (ignoring reserves) • Doesn’t address large price mismatch at peak times • BUT Important assumption about demand responsiveness to prices with varying notification U.C. Energy Institute

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18. Direct Estimation of the Size of Transfers from RTP Adoption • Simple analysis assuming no demand elasticity - estimating pure transfer effect • this is a lower bound on losses due to • ability to respond to price • market price compression due to RTP • Data on realtime consumption of 636 large customers in California • randomly chosen among all large customers • Using a set of realtime prices (actual and simulated), calculate customer costs under breakeven flat rate and under RTP U.C. Energy Institute

19. Changes in Electricity Bills due to RTP(assuming demand of sample customers has zero price elasticity) U.C. Energy Institute

20. How Hedgeable is RTP Risk? • Resistance to RTP due to “risk” • separate from transfers, leaves bill volatility • not risk of sustained high prices, which RTP reduces • Why do large customers care about bill volatility? • Why do publicly traded firms buy insurance? • How much does RTP increase bill volatility? • How much would hedging reduce it? U.C. Energy Institute

21. Empirical Analysis of Hedging • Same data as for analyzing transfers • customer load data, actual and simulated prices • Calculate monthly bills for customers under alternative billing regimes • flat-rate, TOU, RTP, RTP with Hedging • Study monthly bill volatility • Focus on most volatile prices from simulation with very inelastic market demand U.C. Energy Institute

22. Alternative Measures of Volatility • Coefficient of Variation (std dev / mean) under each billing regime • Maximum/Mean bill faced under each billing regime • Ratio of measures under alternative billing regimes • same-customer changes in volatility U.C. Energy Institute

23. Bill Volatility Measured As Standard Deviation of Bill U.C. Energy Institute

24. Bill Volatility Measured As Maximum Bill U.C. Energy Institute

25. RTP and Operating Reserves • RTP will not eliminate the need for reserves • so long as price-responsive demand is slower than callable supply • But RTP offers more than peak demand reduction • demand “tilts” as well as shifts • RTP will gradually reduce use of reserves • as system operators recognize its reliability • Eventually, RTP will reduce the standard for percentage reserves U.C. Energy Institute

26. Conclusions • Conservative estimates of potential welfare gain outweigh implementation costs • Even with very small demand elasticities • Diminishing return to increased elasticity or increased share of population on RTP • TOU is a poor substitute for RTP so long as there is shorter run elasticity of demand • Recent pilot programs indicate there is • Most of the transfer RTP causes are already taking place under TOU • RTP does increase bill volatility compared to TOU, but most of that increase can be eliminated with simple hedging strategies U.C. Energy Institute