Risk-Constrained Strategic Bidding of GenCos Considering Demand Response

This paper presented a combined scheduling and bidding algorithm for constructing the bidding curve of an electric utility that participated in the day-ahead energy markets. Day-ahead market price uncertainty was modeled using non-probabilistic information gap decision theory (IGDT). The considered utility consisted of generation units and a retailer part; the retailer part of the utility and its demand response program (DRP) could affect the utility's profit, which should be considered in the bidding strategy problem. The bidding strategy algorithm proposed in this paper dispatched units by optimizing the demand response programs of the retailer part. In addition, non-decreasing bidding curve was constructed according to the proposed IGDT-based method. Applicability of the proposed method was demonstrated using an illustrative example with 54 thermal units. Results were verified using after-the-fact actual market data.