Bidding Strategies For Generation Companies In The Electricity Market Environment

The power industry worldwide is experiencing unprecedented restructuring for breaking traditional monopoly, introducing competition and establishing electricity markets. The power industry in China is now under the way of restructuring and generation markets are expected to be established. As a result, competition has been introduced in generation sector through bid-based operation. In the new electricity market environments, profits of generation companies depend, to a large extent, on bidding strategies employed. Hence, how to develop optimal bidding strategies has become a major concern of generation companies. This dissertation focuses on the study of bidding strategies of generation companies in the electricity market environment and some significant results are obtained.A comprehensive introduction to building optimal bidding strategies for generation companies is made from several aspects, including approaches, contents, under different market rules and other corresponding problems, in the first part of the thesis, and the emphasis is put on recent research work in this area.Although much research work on developing optimal bidding strategies for generation companies have been done, there are still many difficult problems to be solved. Moreover, bidding strategies need to be updated accordingly with the development of electricity markets, for instance, improvements of electricity market design and modifications of market rules. The thesis focuses on several difficult problems concerning building bidding strategies for generation companies such as step-wise bidding protocols, insufficient historical data, transmission congestion and risk associated with bidding decision. Four models and approaches of developing optimal bidding strategies for generation companies are proposed and numerical simulations made. An application software system, i.e. bidding decision supporting system, is designed in the last part of the thesis.First, a model of bidding strategies based on Zhejiang provincial electricity market in which step-wise bidding rules are utilized is developed. Rival bidding behaviors are described by normal distribution functions, and the problem ofbuilding the optimal bidding strategy for a generation company is then formulated as a stochastic optimization problem, and solved by a Monte Carlo approach.Secondly, a fuzzy set theory based method for building optimal bidding strategies is presented for generation companies participating in recently launched or market structure and auction rules just modified electricity markets in which the available historical data is not sufficient. Taking into account of the insufficient history data especially bidding data, bidding behaviors of rival generation companies are modeled as fuzzy sets and a bidding strategy optimization model is then developed. The well-known genetic algorithm is next employed to solve the bidding strategy optimization problem.Thirdly, the problem of developing optimal bidding strategies for generation companies with transmission network congestion taken into account is systematically investigated, and a methodological framework for solving this problem is established. The proposed method is also served for analyzing the abuse of market power by generation companies.Fourthly, an approach to develop optimal bidding strategies for generation companies in spot market with step-wise bidding function and pay-as-bid settlement rules is presented with associated risk taken into account. Based on estimated rivals' bidding behavior, an probability approach is developed to evaluate the expected profit and risk level of a given bidding strategy, which can help a generation company to make its bidding decision. In addition, according to the generation company's risk preference, an optimal bidding model is built to maximize the utility function and solved by Genetic Algorithm.Fifthly, an integrated design framework for an application software, i.e. bidding decision supporting system, is made to help the decision makers of generation...