Optimal Energy Storage Operation Chart and Output Distribution of Cascade Reservoirs Based on Operating Rules Derivation

Yuxin Zhu, Jianzhong Zhou (), Yongchuan Zhang, Zhiqiang Jiang, Benjun Jia () and Wei Fang
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Yuxin Zhu: Huazhong University of Science & Technology
Jianzhong Zhou: Huazhong University of Science & Technology
Yongchuan Zhang: Huazhong University of Science & Technology
Zhiqiang Jiang: Huazhong University of Science & Technology
Benjun Jia: China Yangtze Power Co., Ltd.
Wei Fang: Huazhong University of Science & Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 14, No 20, 5766 pages

Abstract: Abstract An energy storage operation chart (ESOC) is one of the most popular methods for conventional cascade reservoir operation. However, the problem of distributing the total output obtained from the ESOC has not yet been reasonably solved. The discriminant coefficient method is a traditional method for guiding the output distribution by determining the order of reservoir supply or storage; however, it cannot quantify the water used in operation. Thus, this study develops a new output distribution model using a polynomial fitting method and an artificial neural network to express the functional relationship derived from the deterministic optimization results of long-term runoff series to maximize power generation. Cascade reservoirs of the lower reaches of the Jinsha River in China were selected for the case study. Compared to the discriminant coefficient method, the proposed method can rationally distribute the total output, thus avoiding the problem of concentrated deserting water in downstream reservoirs that occurs in the discriminant coefficient method. In general, this study proposes an effective alternative method to guide cascade reservoir operation.

Keywords: Hydropower operation; Energy storage operation chart; Output distribution; Polynomial fitting method; Artificial neural network; Operating rules derivation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11269-022-03333-8

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