Derivation of water and power operating rules for multi-reservoirs |
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| Authors: | Yanlai Zhou Shenglian Guo Pan Liu Chong-yu Xu Xiaofeng Zhao |
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| Institution: | 1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China;2. Changjiang River Scientific Research Institute, Wuhan, Chinazyl23bulls@whu.edu.cn;4. Department of Geosciences, University of Oslo, Oslo, Norway;5. Hubei Provincial Water Resources and Hydropower Planning Survey and Design Institute, Wuhan, China |
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| Abstract: | ABSTRACTWater operating rules have been universally used to operate single reservoirs because of their practicability, but the efficiency of operating rules for multi-reservoir systems is unsatisfactory in practice. For better performance, the combination of water and power operating rules is proposed and developed in this paper. The framework of deriving operating rules for multi-reservoirs consists of three modules. First, a deterministic optimal operation module is used to determine the optimal reservoir storage strategies. Second, a fitting module is used to identify and estimate the operating rules using a multiple linear regression analysis (MLR) and artificial neural networks (ANN) approach. Last, a testing module is used to test the fitting operating rules with observed inflows. The Three Gorges and Qing River cascade reservoirs in the Changjiang River basin, China, are selected for a case study. It is shown that the combination of water and power operating rules can improve not only the assurance probability of output power, but also annual average hydropower generation when compared with designed operating rules. It is indicated that the characteristics of flood and non-flood seasons, as well as sample input (water or power), should be considered if the operating rules are developed for multi-reservoirs. EDITOR D. Koutsoyiannis ASSOCIATE EDITOR not assigned |
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| Keywords: | multi-reservoirs operating rule progressive optimality algorithm genetic algorithm aggregation–decomposition |
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