Joint Optimal Regulation For Cascade Hydropower Stations Based On MOPSO And Set Pair Analysis Decision-making Approach

In uncertain electric power market environment, the issue of cascade optimal regulation and decision making is a large scale, dynamic, nonconvex and nonlinear discrete multi-objective decision-making problem, with the constraint conditions of electrical market trading rules, hydrological cycle, generation control, power system security and reliability, power demand and consumers' reaction, which is more complicated than the traditional optimal regulation of cascade hydropower system. Although many researchers have been devoted to finding the effective solutions to the above problems, the mutual conflict of complex objectives and the coupling relations between restrictions in valley cascade hydropower system make the problem description and model solving very difficult, which hardly have satisfying solutions and the new improved theories and reliaztions are in urgent need. Consequently, the study of multi-objective decision-making theory and method in complex cascade hydropower system is always the hot issues in academic frontier. The thesis makes a thorough study on the optimal regulation and decision-making theory and method of valley cascade hydropower stations by analyzing the complicacy in valley cascade hydropower system and adopting the complex system theory and modern intelligence evolutionary method. Focusing on the established multi-objective optimal regulation models of valley cascade hydropower system, multi-objective particle swarm optimization algorithm based on adaptive grids and multi-attribution decision-making approach based on Set Pair Analysis are brought forward for the sake of proving the validity of the promoted optimal algorithms when dealing with large scale, multiple objective and complex multiple constraint conditions optimization problem, which also exhibit the predominance of the decision-making approach based on Set Pair Analysis in the uncertain multi-attribution decision-making problem and develop the theory of valley cascade hydropower system optimal regulation and decision making The investigation results are successfully applied in the multi-objective optimal regulation of Three Gorges cascade, which provide scientific basis for decentralized decision and modern management of valley cascade hydropower stations. The study work and innovations are listed as follows:Multi-objective Particle Swarm Optimization based on adaptive super grids (AG-MOPSO) is proposed for the purpose of overcoming the the defects in the existing multi-objective evolutionary algorithms, such as high computational complexity, bad solution diversities and difficulties in dealing with complicated constraints while solving cascade optimal regulation and decision-making problems,.Compared with some representative multi-objective evolutionary algorithms on a set of well-designed test functions, the presented algorithm has stable convergence, good properties of dealing with complex large scale optimization problem and preferable diversity of solutions by employing the methods of density information estimation in Pareto set, pruning Pareto set, and Pareto optimal solution searching based on adaptive super grids algorithm.Aiming at dispelling the limitations of the conventional decision-making methods when dealing with the flexibility, robustness and uncertainties, this thesis investigates into the theories and methods of the matching-degree depiction, ranking methods of connection numbers and the multi-attribution decision-making approach based on generalized Set Pair Analysis. Several typical calculation methods of the interrelated function, ranking methods based on relatively certainty probability power, and multi-attribution decision-making approaches based on connection number decision matrix are developed in the thesis. The contents mentioned above not only develop the basic theory of Set Pair Analysis, but also supply effective tool to solve uncertain multi-attribution decision-making problems.The thesis establishes and implements the model of the multi-objective optimal regulation of cascade hydropower stations based on AG-MOPSO. It employs water level as the decision variable in AG-MOPSO, and converts the problem of solving cascade regulation into unconstrained optimization problem by calculating the water level range of reservoirs in each period of time, which cuts down computing cost and improves convergence rate due to the guaranteeing process of evolution in feasible regions. The non-inferior solution set of the problem of Three Gorges cascade multi-objective regulation are obtained in the end to suply the data preparation for the final multi-attribution decision.Based on the proposed the multi-attribution decision-making approach based on Set Pair Analysis, the thesis realizes preferred ranked choice and order for the multi-objective regulation schemes of cascade hydropower stations, finds out the key factors influencing the decision making results by the stability analysis of the uncertain evolutive factor, and obtains the satisfactory regulation scheme in the end, which meets the requirements of operation environment and the different hierarchy of the decision making, and provides a new effective way to solve the problems of multi-objective regulation and decision making of cascade hydropower stations as well.