Bidding and Cooperation Strategies for Electricity Buyers in Power Markets

Deregulation of electric power industries in recent years has opened many opportunities for electricity buyers. However, the strong influence of network physical constraints may result in economic decisions that adversely affect the interests of the consumers. Compared with the monopolistic economy of yesteryears, electricity buyers may actually be able to influence the market by cooperating with other buyers in the network. This paper presents a coevolutionary approach to investigate individual and cooperative strategies of buyers in a power market, taking fully into account the physical network constraints. First, the study focuses on deterministic cases, where buyers choose their bidding strategies to maximize the profits in different scenarios of playing individually or cooperatively. It is found that, by evolutionary learning, buyers can benefit from cooperation. After that, the uncertain nature of the market is modeled, where buyers find optimal cooperation strategies to hedge against the risk of low payoffs. The payoff distribution problem in cooperative game theory was linked with the optimal coalition generation problem by proving a theorem. The statistically consistent simulation results show that our approach is able to discover interesting cooperation strategies and can be easily extended to practical networks with a large number of buyers.