Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

Xiang Yu, Hui Sun, Hui Wang, Zuhan Liu, Jia Zhao, Tianhui Zhou and Hui Qin
Additional contact information
Xiang Yu: Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 289 Tianxiang Road, Nanchang 330099, Jiangxi, China
Hui Sun: Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 289 Tianxiang Road, Nanchang 330099, Jiangxi, China
Hui Wang: Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 289 Tianxiang Road, Nanchang 330099, Jiangxi, China
Zuhan Liu: Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 289 Tianxiang Road, Nanchang 330099, Jiangxi, China
Jia Zhao: Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 289 Tianxiang Road, Nanchang 330099, Jiangxi, China
Tianhui Zhou: Economic Research Institute, State Grid Jiangxi Power Corporation, 1588 Yingbinbei Road, Nanchang 330043, Jiangxi, China
Hui Qin: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, Hubei, China

Energies, 2016, vol. 9, issue 6, 1-18

Abstract: Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e. , maximizing hydropower generation and minimizing deviation from the outflow lower target to realize the system’s economic, environmental and social benefits during the drought season, are optimized simultaneously. Experimental results demonstrate that the optimization framework helps to robustly derive multiple feasible non-dominated solutions with satisfactory convergence, diversity and extremity in one single run for the case studied.

Keywords: multi-objective optimal operation; hydropower reservoir system; particle swarm optimization; multi-swarm; comprehensive learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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