Deriving Optimal Operating Rules of a Multi-Reservoir System Considering Incremental Multi-Agent Benefit Allocation

Zhuowei Shen, Pan Liu (), Bo Ming, Maoyuan Feng, Xiaoqi Zhang, He Li and Aili Xie
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Zhuowei Shen: Wuhan University
Pan Liu: Wuhan University
Bo Ming: Wuhan University
Maoyuan Feng: Wuhan University
Xiaoqi Zhang: Wuhan University
He Li: Wuhan University
Aili Xie: Wuhan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 11, No 6, 3629-3645

Abstract: Abstract Joint multi-reservoir operation is one of the most efficient measures to meet the demand for increasing economic benefits. Operating rules have been widely used in long-term reservoir operations. However, reservoirs belong to multiple agents in most cases, which imposes difficulties on benefit allocation. This motivated us to derive optimal operating rules for a multi-reservoir system, considering incremental benefit allocation among multiple agents. Fairness of incremental benefits for multiple agents is proposed as one of the objective functions, and then optimal joint operating rules with fairness are derived. The optimal joint operating rules with fairness are compared with conventional, optimal individual, and joint operating rules. The Three Gorges (Three Gorges and Gezhouba) and Qing River (Shuibuya, Geheyan and Gaobazhou) cascade reservoirs are selected for case study. The optimal joint operating rules with fairness not only encourage agents to participate in joint operation, but also increase average annual hydropower generation and the assurance rate of hydropower generation relative to those of the conventional operating rules. Furthermore, the proposed optimal operating rules with fairness are easier to implement in practice than the optimal joint rules. This indicates that the proposed method has potential for improving operating rules of a multi-reservoir system.

Keywords: Reservoir operation; Optimal operating rules; Benefit allocation; Multi-agent (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-018-2010-9

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