The Research On Bidding In Power Market Based On Immune Genetic Algorithm And Particle Swarm Optimization Algorithm

Since 1999, our country implemented the policy of"separate the generation and transmission management at a competitive price",the power market reforms began in China. The power market is not only open to the electric power industry has injected new vitality, but also to the power system on introduction of a series of new problems. The bidding algorithm in power market is one of the important aspects and its difficulty lies in freeing from its restraint and achieve viable solutions, at the same time reflected interests allocated to the power market participants in order to ensure that the power system operation"safety, quality, and the economy".At first, according to characteristics and development requirements of the power market, this thesis researches on various bidding method of power market, and sums up their respective advantages and disadvantages; secondly, the bidding models and principle of power market has been described detailed; and then, the particle swarm optimization algorithm steps and several improvements to the form has been introduced,and method on immune genetic algorithm; at last, the thesis presents the particle swarm optimization algorithm combined with the immune algorithm to solve the bidding method of the power market.At present, there are so many types of generation bidding algorithms commonly used in deregulation environment, like the equal bidding price method, the dynamic programming method, genetic algorithm and so on. But any method couldn't solve all of problem effectively and achieve the optimum solution, because they can be used to solve different bidding curves respectively and deal with various sets of constraints. IA-PSO, combined by particle swarm optimization algorithm and immune algorithm, has been explored for bidding of power market in the thesis, which confirmed the encoding style, fitness function, the renewing method of immune particles, the method to get the immunity and the renewing method of the antibody memory bank. The algorithm combines the PSO's simple and easy to achieve and the overall immune mechanism search capabilities to accelerate the convergence rate, improving the ability of the global convergence. The thesis also proposes a method, which is particles are divided into two groups to search of different regions of space in order to speed up search speed. The completion strategy decided the renewing of the particles which aren't satisfied constraints in the two populations. The strategy defines the three operator (substitute operator, annexation operator and separatist operator). In addition, the paper redesign the adaptive inertia weight designed by the fitness value. With the value to change, at the start of the algorithm, the IA-PSO search larger regional, and later it starts fine local search.Through the simulation results, the hybrid algorithm for bidding of power market show that the bidding algorithm can achieve the desired effect and feasible and effective, thereby provides a new way of thinking and methods to the research of bidding algorithm in power market.