An Evolutionary Computation for Supplier Bidding Strategy in Electricity Auction Market
In the past decade, the electric utility industry in many countries around the world has been undergoing fundamental structural changes to introduce competition and enhance efficiency. The traditional vertically integrated utility is deregulated to open up the system to the market, in response to the pressures of privatization and customer demands. Electricity and services can be sold and purchased as a commodity through different market structures. Under this deregulated and competitive environment, economics and profitability have become the major concern of every electric supplier, and each supplier will act in his/her own selfinterest in this new environment. Among the proposed market structures, the electric auction market has been widely experienced and implemented in different countries with different protocols. Market participants-electric suppliers, and distribution companies-are required to submit their sealed bids to the auction market to compete for power energy. All participants winning the auction will be paid based on the rules agreed upon by the participants. Thus the bidding strategy which is essential for a successful business in this auction market is becoming one of the most important issues in the electric industry. Market participants can improve their benefits dramatically by strategic bidding. Developing bidding strategies for competitive suppliers have been studied by many researchers in recent years. Game theory [1] is naturally the first choice to deal with this issue, and much work has been done using this traditional theory. In [2], a Nash game approach is used to study the pricing strategy in the deregulated power marketplace, where each participant has incomplete information about his rivals. A method, which uses Cournot non-cooperative game theory to determine the optimal supply quantity for each power producer in an oligopoly electricity market, is presented in [3]. The results show that the estimation accuracy of production cost functions of rivals plays an important role in this market. Different electricity market rules and their effects on bidding behaviors in a non-congestion grid are analyzed in [4]. The authors conclude that generators can take advantage of congestion in their strategic bidding behavior. But game theory is not the only solution to this problem. In fact, due to the complexity, dynamics and uncertainty of the restructured electricity market, evolutionary computation algorithms and reinforcement learning are receiving increasing attention recently and are becoming major tools in solving this problem. A genetic algorithm is developed in [5] to evolve the bidding strategies of participants in a double auction market. Markov Decision Process is used to optimize the bidding decisions to maximize the expected reward over a planning horizon in [6], The optimal bidding problem is modeled as a stochastic optimization problem in