Title: Mechanism Design for Demand Response Programs

Abstract: Demand Response (DR) programs serve to reduce the demand for electricity at times when the supply is scarce and expensive. In a typical DR program, the manager of the program recruit consumers for meeting load reduction targets. The recruited consumers are notified several hours in advance of an upcoming event when the consumers would be called for curtailing their load (DR event). The consumers are paid for curtailing their load from a contractually established baseline when such (DR) events are called. Baseline is an estimate of the load a consumer would have consumed had there not been a DR event. The payment for load curtailment creates incentives for inflating the baseline assigned to a consumer. There are several cases of consumers gaming their baseline for economic benefit. I will discuss a novel approach, self-reported baseline mechanism (SRBM) for aggregating demand response. Under SRBM, each consumer reports a baseline and a marginal utility for consumption. These reports are strategic and need not be truthful. Based on the reported information, the aggregator selects and calls certain consumers for meeting the load reduction target of a DR event. The consumers called are paid for the observed reductions from their self-reported baselines. Consumers who are not called face penalties for consumption shortfalls from their baselines. Overall, the mechanism is specified by the probabilities of calling the consumers, payment per unit of load reduction and the penalty for those who are not called. The SRBM mechanism guarantees that truthful reporting of baseline and marginal utility is a dominant strategy. Thus, SRBM eliminates the incentive for agents to inflate baselines. SRBM is also assured to meet the load reduction target and is fair from the perspective of participating consumers. I will also show that SRBM is almost optimal in terms of the average cost of DR provision, under certain class of distributions.

Speaker’s bio: Deepan Muthirayan is currently a Visiting Faculty at Plaksha University, Mohali and a Post-doctoral Researcher in the department of Electrical Engineering and Computer Science at the University of California at Irvine, where his work is focused on machine learning and control, and Cyber-Physical Systems. He obtained his PhD from the department of Mechanical Engineering at the University of California at Berkeley (2016) and Dual Degree (B.Tech/M.tech) from the Indian Institute of Technology Madras (2010). His doctoral thesis work focused on market mechanisms for integrating demand flexibility in energy markets. Before his term at UC Irvine, he was a post-doctoral associate in the department of Electrical and Computer Engineering at Cornell University, where his work focused on optimization, parametric learning and matching markets. His research spans the areas of control systems, topics at the intersection of learning and control, machine learning, online learning and optimization, game theory, mechanism design and their application to cyber-physical systems.