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Article Excerpt
In PJM, 15% of electric generation capacity ran less than 96 hours, 1.1% of the time, over 2006. If retail prices reflected hourly wholesale market prices, customers would shift consumption away from peak hours and installed capacity could drop. We use PJM data to estimate consumer and producer savings from a change toward real-time pricing (RTP) or time-of-use (TOU) pricing. Surprisingly, neither RTP nor TOU has much effect on average price under plausible short-term consumer responses. Consumer plus producer surplus rises 2.8%-4.4% with RTP and 0.6%-1.0% with TOU. Peak capacity savings are seven times larger with RTP. Peak load drops by 10.4%-17.7% with RTP and only 1.1%-2.4% with TOU. Half of all possible customer savings from load shifting are obtained by shifting only 1.7% of all MWh to another time of day, indicating that only the largest customers need be responsive to get the majority of the short-run savings.
1. INTRODUCTION
The electricity industry uses much of its generation and transmission capacity only a small fraction of the time. Over the calendar year 2006, 15% of the generation capacity in the Pennsylvania-New Jersey-Maryland (PJM) territory ran less than 1.1% of the time (96 hours or less), and 20% of capacity ran less than 2.3% of the time (202 hours or less) (PJM 2007a). (1) The result is tens of billions of dollars (2) invested in peaking generation that has low capital cost, but high generation cost and life cycle social cost.
The excessive peaking capacity has two causes. The first is technical: there must be enough system capacity to satisfy demand at all times or there will be a blackout. The second is regulatory: most customers pay a constant flat price for power rather than responding to the changing hourly price of the wholesale market. Flat-rate customers have no incentive to shift consumption away from times of peak demand. For example, a customer whose retail rate is $0.10/kWh will pay the same price no matter whether the wholesale price reaches its limit of $1/kWh during peak demand or drops to $0/kWh during trough demand. If customers instead faced the changing wholesale price of electricity, as they do with most products including gasoline, natural gas, fruits, and vegetables, then they would buy less power at $1/kWh and more at $0/kWh. In doing so they would flatten demand over time, enabling society to diminish investments in peaking generators, instead increasing use of base-load units that have lower generation costs.
Some electricity customers face "time of use" (TOU) pricing that charges them a higher price during on-peak hours, with the fixed on-peak and off-peak rates calculated as the delivered cost averaged over a year. A few customers face "real time pricing" (RTP) where the hourly wholesale generation price determines the retail price. The TOU price gives better information and incentives than a single fixed tariff, but does not account for the times when wholesale prices spike because of high demand or equipment problems. Some view a TOU rate as a good compromise that frees customers from having to be informed about constantly changing prices and adjusting their consumption accordingly.
Few end users have any opportunity to react to real-time market conditions or to the location-specific costs of generation and transmission. A PJM survey of load-serving entities (LSE) reported that only 4.7% of end user MW are on rates directly or indirectly related to the real-time or day-ahead locational marginal price (LMP) (PJM 2005, 2006a). (3) Companies currently offering RTP rates usually have a variety of partial-hedging options as well (Barbose, et al. 2004). Some additional customers are enrolled in direct load control, interruptible contracts, or other subsidy programs that offer curtailment incentives during the top few load hours per year. A Federal Energy Regulatory Committee (FERC) report estimates that 4% of peak MW in ReliabilityFirst Corporation (RFC) territory (4) could potentially have been curtailed via either RTP rates or non-price response programs, but the maximum response in 2005 was only 0.7% of MW (FERC 2006a). Actual reductions are usually much smaller than program enrollments, partly because reduction is often voluntary (Heffner 2002).
We view the current flat tariff as both inefficient and inequitable. It is inefficient because it raises system costs and requires much more capital equipment to deliver the same quantity of power. It is inequitable, by our definition, because flat and counter-cyclical customers subsidize customers with high coincident peak demand.
We present a short-run analysis of a change to a more responsive demand-side market. In Section 4, we use one year of PJM data to build a supply model that implicitly accounts for dispatch constraints and varying conditions observed over a year. We use this model in three different simulations to estimate the impacts of responsive load. The first in Section 5 is an assumed load-shifting scenario that finds the effects of small changes in load profile on overall price. The load-shifting simulation does not consider customer time preference, but does show how quickly savings could be achieved. The final two simulations in Section 6 are more realistic; they use hourly demand curves to predict short-run impacts from change toward TOU or RTP from flat-rate pricing.
2. LITERATURE REVIEW
Borenstein's long-run RTP analysis predicts more than double the peak load savings we predict in our short-run analysis, see Section 6 (Borenstein 2005). His conclusion results from using a long-term supply curve in estimating hourly equilibrium conditions. Because Borenstein includes capital costs in his supply curves, he predicts hourly prices up to $90,772/MWh; this implies that 22% of the annual bill is accounted for from the top hour. Those high wholesale prices would only be possible if market rules change dramatically since hourly prices are hard-capped at $1000/MWh (5) in all but one United States market and determined based on short-run conditions with a fixed generation portfolio (FERC 2006c). Further, we believe that Borenstein's exercise is intended to be primarily illustrative on peak load reductions since his resulting load duration curves are abruptly leveled off on the high end. Our short-run analysis reflects current PJM conditions because we use observed market data.
Holland and Mansur predict less than half the short-term peak load savings that we predict from RTP, see Section 6 (Holland, et al. 2004, 2006). The modest impact is due to their method of using one constant stacked marginal cost curve to represent supply over the entire year. (6) We use observed market prices to account for transmission and other constraints (7) while they assume constraint-free economic dispatch of system generators to estimate marginal cost. Holland and Mansur attempt to correct for one of these constraints, generator availability, by discounting the capacity of each generator by an expected "outage" factor, but the method cannot capture the observed phenomenon of very high prices at moderate demand levels. Based on our own empirical analysis, we find that a constraint-free stacked marginal...
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