A Bayesian game theoretic based bidding strategy for demand response aggregators in electricity markets

In recent years, significant development in smart metering and remote sensing systems in the electricity industry, especially on the side of consumers, it has made in implementation demand response programs in peak periods possible. The present study aims to present a game theoretical approach to the optimal bidding strategies for demand response (DR) aggregators in deregulated energy market. This model is based on the customer benefit function and price elasticity so that an economic responsive load model is applied to DR implementation. In this paper, the interaction between a system operator and aggregators in a deregulated market is modeled in this paper, where DR aggregator provides DR service to the system operator. It is assumed that a system operator collects bids from DR aggregators and determines each aggregator share in the demand response programs by maximizing its revenue function, and also, offers rewards to DR aggregators to reach this goal. On the other hand, DR aggregators compete together to offer their DR services to the network operator and in this way provide compensation for customers. The competition between DR aggregator participants is modeled as a noncooperative game considering incomplete information. This game is solved using the Nash equilibrium idea. By the implementing the proposed method, the operator's profit rises up 7 percent