Demand Response (DR) programs serve to reduce the consumption of electricity at times when the supply is scarce and expensive. The utility informs the aggregator of an anticipated DR event. The aggregator calls on a subset of its pool of recruited agents to reduce their electricity use. Agents are paid for reducing their energy consumption from contractually established baselines. Baselines are counter-factual consumption estimates of the energy an agent would have consumed if they were not participating in the DR program. Baselines are used to determine payments to agents. This creates an incentive for agents to inflate their baselines. We propose a novel self-reported baseline mechanism (SRBM) where each agent reports its baseline and marginal utility. These reports are strategic and need not be truthful. Based on the reported information, the aggregator selects or calls on agents to meet the load reduction target. Called agents are paid for observed reductions from their self- reported baselines. Agents who are not called face penalties for consumption shortfalls below their baselines. The mechanism is specified by the probability with which agents are called, reward prices for called agents, and penalty prices for agents who are not called. Under SRBM, we show that truthful reporting of baseline consumption and marginal utility is a dominant strategy. Thus, SRBM eliminates the incentive for agents to inflate baselines. SRBM is assured to meet the load reduction target. SRBM is also nearly efficient since it selects agents with the smallest marginal utilities, and each called agent contributes maximally to the load reduction target. Finally, we show that SRBM is almost optimal in the metric of average cost of DR provision faced by the aggregator.
Traditional DR programs reward participating agents for load reduction during peak demand periods. Agents have an incentive to deliberately inflate their baselines to increase the payments they receive @cite_21 , @cite_0 , @cite_20 , @cite_13 . Alternative baseline mechanisms (ex: aggregate baselines) which improve market efficiency are offered in @cite_0 . These do not explicitly address baseline inflation concerns. Adverse selection and double payment effects are two other issues that arise from rewarding agents based on estimated baselines @cite_21 .
@cite_0 @cite_21 @cite_13 @cite_20
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Recently issued U.S. Federal Energy Regulatory Commission regulations require comparable treatment of demand reduction and generation in the wholesale electric market so that they are compensated at the same market clearing price. The new regulations measure demand reduction as a reduction from a “customer baseline,” a historically based estimate of the expected consumption. In this paper, we study the incentive effects on the efficiency of the demand response regulation using a static equilibrium model and a dynamic extension of the model. Our analysis provides three main results. Firstly, our analysis shows that the demand reduction payment will induce consumers to (1) inflate the customer baseline by increasing consumption above the already excessive level during normal peak periods and (2) exaggerate demand reduction by decreasing consumption beyond the efficient level during a demand response event. This result persists when applied to alternative baseline designs in a dynamic model. Secondly, we study alternative policy remedies to restore the efficiency of demand response regulation and introduce a new approach to define the customer baseline as a fixed proportion of an aggregate baseline. In particular, the aggregate baseline approach can significantly weaken or eliminate the incentive to inflate the baseline. Finally, we illustrate that if the baseline inflation problem is solved and demand and supply functions are linear, the current policy can produce a net social welfare gain. However, the welfare improvement requires that demand reduction be paid only when the wholesale price is at least twice the fixed retail rate. This argues that the policy should include a sufficiently high threshold price below which demand response is not dispatched. Price-responsive demand is essential for the success of a smart grid. However, existing demand-response programs run the risk of causing inefficient price formation. This problem can be solved if each retail customer could establish a contract-based baseline through demand subscription before joining a demand-response program. This paper analyzes the results of a critical peak pricing (CPP) experiment involving 123 residential customers of the City of Anaheim Public Utilities (APU) over the period June 1, 2005 to October 14, 2005. Using a nonparametric condition mean estimation framework that allows for customer-specific fixed effects and day-of-sample fixed effects, I find that customers in the treatment group consumed an average of 12 percent less electricity during the peak hours of the day on CPP days than customers in the control group. There is also evidence that this reduction in consumption for customers in the treatment group relative to customers in the control group is larger on higher temperature CPP days. The impact of CPP events is confined to the peak periods of CPP days. Mean electricity consumption by customers in the treatment group is not significantly different from that of customers in the control group during the peak or off-peak periods of the day before or day after a CPP event. Much of the estimated consumption reduction of treatment consumers relative to control group consumers during peak periods of CPP days is due to reductions from a higher level of consumption by treatment group customers in non-CPP days. The consumption reductions paid rebates during CPP days are almost 7 times the reduction in consumption due to CPP events predicted by the treatment effects estimate, which provides strong evidence of an overly generous method for setting the reference level for peak period consumption relative to which customers are issued refunds during CPP days. The paper closes with a discussion of the challenges associated with implementing a CPP rate with a rebate mechanism as the default rate for residential customers. There is a significant risk of creating conditions that will crowd out true price response by focusing too much on demand response programs with unverifiable baselines and reliability-based rather than price-based mechanisms for obtaining consumption reductions.