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Energies 2016, 9(6), 438; doi:10.3390/en9060438

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

1
Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 289 Tianxiang Road, Nanchang 330099, Jiangxi, China
2
Economic Research Institute, State Grid Jiangxi Power Corporation, 1588 Yingbinbei Road, Nanchang 330043, Jiangxi, China
3
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, Hubei, China
*
Author to whom correspondence should be addressed.
Academic Editor: John Ringwood
Received: 29 March 2016 / Revised: 19 May 2016 / Accepted: 2 June 2016 / Published: 7 June 2016
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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. View Full-Text
Keywords: multi-objective optimal operation; hydropower reservoir system; particle swarm optimization; multi-swarm; comprehensive learning multi-objective optimal operation; hydropower reservoir system; particle swarm optimization; multi-swarm; comprehensive learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yu, X.; Sun, H.; Wang, H.; Liu, Z.; Zhao, J.; Zhou, T.; Qin, H. Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization. Energies 2016, 9, 438.

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