Strategic Generation Capacity Expansion Planning With Incomplete Information

To study the competitive behavior among individual generating companies (GENCOs), an incomplete information game model is proposed in this paper in which each GENCO is modeled as an agent. Each agent makes strategic generation capacity expansion decisions based on its incomplete information on other GENCOs. The formation of this game model falls into a bi-level optimization problem. The upper level of this problem is the GENCOs' own decision on optimal planning strategies and energy/reserve bidding strategies. The lower-level problem is the ISO's market clearing problem that minimizes the cost to supply the load, which yields price signals for GENCOs to calculate their own payoffs. A co-evolutionary algorithm combined with pattern search is proposed to optimize the search for the Nash equilibrium of the competition game with incomplete information. The Nash equilibrium is obtained if all GENCOs reach their maximum expected payoff assuming the planning strategies of other GENCOs' remain unchanged. The physical withholding of capacity is considered in the energy market and the Herfindahl-Hirschman index is utilized to measure the market concentration. The competitive behaviors are analyzed in three policy scenarios based on different market rules for reserve procurement and compensation.