Demand Response (DR) programs serve to reduce the demand for electricity at times when the supply is scarce and expensive. Consumers with flexible consumption profiles are recruited by an aggregator who manages the DR program. Participating consumers are given several hours advance notice to reduce their consumption during an upcoming DR event. They are paid for reducing their consumption from a contractually established baseline during this DR event. Baselines are counterfactual 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 in order to increase the payments they receive. There are several cases of consumers gaming their baseline for economic benefit. We proposed a novel approach self-reported baseline mechanism (SRBM). 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 certain 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 from their baselines. The mechanism is specified by the design of 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 and is fair from the perspective of participating agents. Finally, we show that SRBM is almost optimal in the metric of average cost to the aggregator of DR provision per KWh.
Deepan Muthirayan is currently a post-doctoral researcher at Cornell, working with Prof. Eilyan Bitar. Prior to Cornell, he completed his Phd from U C Berkeley, where his thesis work focussed on integrating demand response in power system markets. Currently he is working on learning consumer response with baseline and online algorithms for energy market platforms. His research interests lies in Stochastic control, Mechanism design, Learning theory, Online algorithms and their application to Cyber-Physical Systems.