Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm

被引:51
作者
Ahmadianfar, Iman [1 ]
Samadi-Koucheksaraee, Arvin [1 ]
Bozorg-Haddad, Omid [2 ]
机构
[1] Behbahan Khatam Alanbia Univ Technol, Dept Civil Engn, Behbahan, Iran
[2] Univ Tehran, Coll Agr & Nat Resources, Fac Agr Engn & Technol, Dept Irrigat & Reclamat Engn, Tehran, Iran
关键词
Multi-reservoir; Differential evolution; Optimization; Reservoir; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHMS; MULTIRESERVOIR SYSTEMS; MATING OPTIMIZATION; OPTIMAL OPERATION; HBMO ALGORITHM; MANAGEMENT;
D O I
10.1007/s11269-017-1753-z
中图分类号
TU [建筑科学];
学科分类号
081407 [建筑环境与能源工程];
摘要
Deriving the optimal policies of hydropower multi-reservoir systems is a nonlinear and high-dimensional problem which makes it difficult to achieve the global or near global optimal solution. In order to optimally solve the problem effectively, development of optimization methods with the purpose of optimizing reservoir operation is indispensable as well as inevitable. This paper introduces an enhanced differential evolution (EDE) algorithm to enhance the exploration and exploitation abilities of the original differential evolution (DE) algorithm. The EDE algorithm is first applied to minimize two benchmark functions (Ackley and Shifted Schwefel). In addition, a real world two-reservoir hydropower optimization problem and a large scale benchmark problem, namely ten-reservoir problem, were considered to indicate the effectiveness of the EDE. The performance of the EDE was compared with the original DE to solve the three optimization problems. The results demonstrate that the EDE would have a powerful global ability and faster convergence than the original DE to solve the two benchmark functions. In the 10-reservoir optimization problem, the EDE proved to be much more functional to reach optimal or near optimal solution and to be effective in terms of convergence rate, standard deviation, the best, average and worst values of objective function than the original DE. Also, In the case of two-reservoir system, the best values of the objective function obtained 93.86 and 101.09 for EDE and DE respectively. Based on the results, it can be stated that the most important reason to improve the performance of the EDE algorithm is the promotion of local and global search abilities of the DE algorithm using the number of novel operators. Also, the results of these three problems corroborated the superior performance, the high efficiency and robustness of the EDE to optimize complex and large scale multi-reservoir operation problems.
引用
收藏
页码:4375 / 4397
页数:23
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