Multi-objective Optimal Operation And Multi-attribute Decision Making Of Large-Scale Cascaded Hydropower Stations

As a complex nonlinear large scale system, the optimal operation of large scale cascade hydroelectric stations is a high dimensional constrained optimal multi-objective problem with complex characteristics such as strong-coupling of the decision variables and nonlinear objective functions. This problem is affected by large amount of complex hydrological and electrical factors, thus it is an interdisciplinary research field in hydro science and complex system engineering, which is also attracting more and more attention of the researchers. Nowadays, cascade hydroelectric stations not only undertake the water supply responsibility for the area, but also play an important in power generation, ecological improvement of the river basin area and flood control. Hence, there are great benefits if the joint operation can be implemented by those large scale cascade hydroelectric stations. However, along with the rapidly exploitation of hydropower resources, the topological structure as well as the hydraulically and electrical relationship among cascade hydroelectric stations become more and more complex, moreover, the conflicts among multiple operational objective are more obliviously. Traditional optimal operation theories and techniques can not fully exert the comprehensive benefit of the large-scale cascade hydroelectric stations, and new joint operation and decision-making theories and methods are necessitated in present. In this thesis, aiming at the joint optimal operation problems of large-scale cascade hydroelectric stations, we adopt complex system modeling, multi-objective optimization and decision-making theory as well as intelligent evolutionary optimal method into our research, and a series of research results in joint optimal operation and decision-making of large-scale cascade hydroelectric stations are obtained, The main content of the innovation and contribution in this thesis are listed as follows:(1) Considering the complex characteristics of the joint optimal operation of large-scale cascade hydroelectric stations, we establish optimal models of large-scale cascade hydropower stations of upstream cascaded reservoirs of Yangtze River and Impounding dispatch for the lower cascade reservoirs as well as optimal dispatch of hydro-thermal system. Meanwhile, in order to overcome the limitation of existing theories and models when applied to solve practical constrained operational problems, a modified differential evolution method (MDE) which is designed for solving the optimal operation of cascade hydropower stations with coupled decision variables while avoiding " curse of dimensionality " efficiently is proposed. The proposed method adopts differential evolution as the basic framework for solving the problem, and dynamic parameter adjust strategy as well as local search operator based on chaotic sequences are introduced into the modified method to improve the optimal capacity. The obtained results show that MDE can solve those problem with fast convergence rate and high precision while handing the complex constraints effectively, thus we provide a new efficient way to solve optimal operation problems of large-scale cascade hydroelectric stations.(2) By analyzing the complex features of the optimization problem with multiple objectives, this thesis introduce the concept of Pareto optimal to discusses the theoretical basis of the multi-objective joint operation modeling, and a modified multi-objective differential evolution (MMODE) is presented to solve multi-objective problems (MOPs) with complex constraints. In MMODE, we adopt archive technology to store Pareto optimal solutions during the searching process, and the size of archive set is maintained by using a iterative strategy which is based on proposed "μ+1" selection operation. Meanwhile, a chaotic multi-objective local search operator based on Tent chaotic sequence is designed to improve the efficiency of the proposed method. MMODE is tested by a series of widely used benchmark MOPs, and the comparing case study is implemented by analyzing the results obtained by MMODE and the results obtained by some other multi-objective optimal algorithms, The results verify the high efficiency and accuracy of the proposed method when dealing with MOPs with complex characteristics.(3) In view of the conflict and competitive relationships among multiple operational requirements with Jinsha river downstream cascade project and Three Gorges cascade project, based on correlation analysis of multiple operational objectives, we establish a multi-objective flood control model, a multi-objective power generation model, and a multi-objective ecological dispatch model to consider multiple dispatch requirements of the cascade hydropower stations at the same time under different schedule modes. Afterwards, the proposed MMODE method is applied to solve these multi-objective models. The results show that MMODE can deal with above multi-objective operation model effectively, and a set of non-dominated dispatch schemes can be obtained rapidly, and based on which we analyze the mutual influence among those operational objectives, thus providing alternatives dispatch schemes for operational decision makers.(4) In order to determine the multi-objective comprehensively optimal operation scheme quickly, scientifically and reasonably, this thesis combined subjective and objective preference during the decision-making process comprehensively and a novel decision-making method based Vague set is proposed. The standard attribute importance matrix is established to quantify their difference to achieve subjective preference, and a modified entropy calculating equation is proposed to calculate entropy weight precisely. Meanwhile, we describe the closeness degree between the alternative schemes and the ideal schemes by using Vague value. By applying the proposed method to solve the multi-objective power generation, flood control and ecological dispatch decision-making problem and analyzing the decision result, we obtain the optimal scheme under different operational situations and preferences rapidly and correctly.