The Research And Realization Of Multi-objective Optimal The Tree Gorge Reservoir Dispatching

Multi-objective optimal scheduling of Cascade Reservoir is a highly complex risk decision making problem which is multi-stage, multi-level, multi-target, and multi-person. In addition, many problems with semi-structured and unstructured nature, so scheduling decisions must be made full use of practical experience and expertise, making it more scientific and democratic. Meanwhile, along with the gradual formation of the electricity market and increasingly large scale of Cascade reservoirs, multi-objective optimization of the scheduling problem solving becomes more difficult, we must constantly explore new theories and methods of decision making. Based on the analysis of existing scheduling problems'property and used of systems engineering theory and Modern intelligent evolutionary approach, multi-objective optimization algorithm, multi - attribute decision-making approach has been researched, which provide a new and effective approach in solving Optimal scheduling problem for the Three Gorges Cascade Reservoir. Specific content summarized as follows:First, in the first chapter the subject of the source, purpose and significance is introduced. Multi-objective optimization algorithm, multi - attribute decision making and multi-objective optimal scheduling of Cascade Reservoir in the present situation and future trend of development has been summarized. In the second chapter, sets up the multi-objective optimization model for the Three Gorge reservoir scheduling. In the third chapter, a multi-objective genetic algorithm based on immunity is proposed (MOIGA). The core idea of this algorithm is designing specific immune operator which can enhance the diversity of groups to combine with GA. It utilizes some characteristics and knowledge in the pending problems and used global and parallel search under the guidance of antibodies to replace blind and random search in genetic algorithm. Since the random search is completed under the guidance in identifying ways, compared with MOGA which is absence of immune operator, this algorithm can greatly increase the converging speed and restrain the degenerate phenomenon effectively. The global convergence of the algorithm is proved. It also said that two operators-crossover and mutation is one of the key factors leading GA into local search. So the combination of genetic algorithms and immune mechanism can be able to greatly improve the performance of GA search. The fourth chapter describes the methods based on fuzzy TOPSIS theories and applications. An effective solution of multi-attribute decision making problems which contain linguistic variables has been given. Finally, with the background of the Three Gorges scheduling problem, non-inferior solutions have been got. With the application of methods based on fuzzy TOPSIS multi - attribute decision making, non-inferior solutions have been sorted. The solutions which can satisfy policy makers have been obtain to guide the actual operation of the Three Gorges reservoir.