Multi-Objective Optimization of the Proposed Multi-Reservoir Operating Policy Using Improved NSPSO

被引:0
|
作者
Xuning Guo
Tiesong Hu
Conglin Wu
Tao Zhang
Yibing Lv
机构
[1] Wuhan University,State Key Laboratory of Water Resources and Hydropower Engineering Science
[2] Changjiang Water Resources Commission,Changjiang Institute of Survey, Planning, Design and Research
[3] Yangtze University,School of Information and Mathematics
来源
Water Resources Management | 2013年 / 27卷
关键词
Multi-reservoir operating policy; Parametric rule; Hedging rule; Water supply; I-NSPSO;
D O I
暂无
中图分类号
学科分类号
摘要
Severe water shortage is unacceptable for water-supply reservoir operation. For avoiding single periods of catastrophic water shortage, this paper proposes a multi-reservoir operating policy for water supply by combining parametric rule with hedging rule. In this method, the roles of parametric rule and hedging rule can be played at the same time, which are reducing the number of decision variables and adopting an active reduction of water supply during droughts in advance. In order to maintain the diversity of the non-dominated solutions for multi-objective optimization problem and make them get closer to the optimal trade-off surfaces, the multi-population mechanism is incorporated into the non-dominated sorting particle swarm optimization (NSPSO) algorithm in this study to develop an improved NSPSO algorithm (I-NSPSO). The performance of the I-NSPSO on two benchmark test functions shows that it has a good ability in finding the Pareto optimal set. The water-supply multi-reservoir system located at Taize River basin in China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of the I-NSPSO. The operation results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for the periods of droughts. And the I-NSPSO also shows a good performance in multi-objective optimization of the proposed operating policy.
引用
收藏
页码:2137 / 2153
页数:16
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