Optimizing Multiple Linear Rules for Multi-Reservoir Hydropower Systems Using an Optimization Method with an Adaptation Strategy

Iman Ahmadianfar (), Omid Bozorg-Haddad () and Xuefeng Chu ()
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Iman Ahmadianfar: Behbahan Khatam Alanbia University of Technology
Omid Bozorg-Haddad: University of Tehran
Xuefeng Chu: North Dakota State University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2019, vol. 33, issue 12, No 14, 4265-4286

Abstract: Abstract Water resources crisis has a significant impact on hydropower energy production, which highlights the importance of water resources management. Reservoirs are effective and powerful systems to manage water resources. Due to the growing water demands and the limited water resources, optimizing these systems to maximize the production of hydropower energy is an essential task. In this study, an effective differential evolution (DE) algorithm with mutation strategy adaptation (MSA-DE) is developed to promote the local and global search capabilities in a feasible domain. In addition, an elitist strategy is applied to escape local optimum trap. In the present study, the MSA-DE algorithm was applied to optimize the multiple linear rules for two multi-reservoir operation systems (3- and 4-reservoir systems) in Iran. In the 3-reservoir system, the best objective function value in 10 runs was 123.57 for the MSA-DE, while the corresponding values for the DE, artificial bee colony (ABC), and genetic algorithm (GA) were 126.42, 147.38, and 126.68, respectively. For the 4-reservoir system, the best objective function values for the MSA-DE, DE, ABC, and GA were 130.50, 159.75, 174.41, and 140.63, respectively. The results demonstrated that the MSA-DE algorithm can be used to derive optimal operating rules for multi-reservoir systems by enhancing appropriate solutions, while it still preserves the accuracy and efficiency of the solutions.

Keywords: Multi-reservoir hydropower system; Multiple linear rule; Differential evolution; Adaptation strategy; Optimization (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11269-019-02364-y

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