Reverse auction-based demand response program: A truthful mutually beneficial mechanism

Matching power demand during peak load hours is a well-known problem in power systems. In fact, the cost of producing electricity increases very rapidly when the demand is high, due to the need for starting backup generators and enhancing transmission system. Incentive-based Demand Response (DR) program is a new approach, enabled by recent advances in smart grid technologies, designed to deal with such problem. According to DR, the utility company can provide economical incentives to users in order to temporarily reduce their energy consumption during peak hours. It is, however, challenging to determine the procedure to distribute such incentives, as well as to ensure that users will be sufficiently engaged and satisfied to make the DR program effective. In this paper, we propose a reverse auction mechanism to enable an incentive-based DR program. We formulate the DR reverse auction as an integer linear programming (ILP) problem, which integrates a perceived-value utility, to model the user perception of electrical appliances, as well as the financial objectives of the utility company. We adopt a Vickrey-Clarke-Groves (VCG) based reverse auction mechanism to guarantee the truthfulness and individual rationality properties. Since the VCG auction requires to optimally solve the NP-Hard ILP problem, we propose a heuristic algorithm named Reverse Auction DemAnd Response (RADAR), and prove that RADAR preserves truthfulness. Extensive simulations using real power consumption data of several homes show that RADAR is effective in reducing demand peaks while outperforming previous solutions in terms of users' perceived utility.