Water Science and Engineering 2014, 7(4) 433-445 DOI:   10.3882/j.issn.1674-2370.2014.04.008  ISSN: 1674-2370 CN: 32-1785/TV

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Keywords
reservoir operation
joint operating rules
simulation-optimization model
improved particle swarm optimization
Authors
Hong-bin FANG
Tie-song HU
Xiang ZENG
Feng-yan WU
PubMed
Article by Hong-bin FANG
Article by Tie-song HU
Article by Xiang ZENG
Article by Feng-yan WU

Simulation-optimization model of reservoir operation based on target storage curves

Hong-bin FANG1, Tie-song HU*1, Xiang ZENG1, Feng-yan WU2

1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P. R. China
2. Hubei Water Resources Research Institute, Wuhan 430070, P. R. China

Abstract

This paper proposes a new storage allocation rule based on target storage curves. Joint operating rules are also proposed to solve the operation problems of a multi-reservoir system with joint demands and water transfer-supply projects. The joint operating rules include a water diversion rule to determine the amount of diverted water in a period, a hedging rule based on an aggregated reservoir to determine the total release from the system, and a storage allocation rule to specify the release from each reservoir. A simulation-optimization model was established to optimize the key points of the water diversion curves, the hedging rule curves, and the target storage curves using the improved particle swarm optimization (IPSO) algorithm. The multi-reservoir water supply system located in Liaoning Province, China, including a water transfer-supply project, was employed as a case study to verify the effectiveness of the proposed join operating rules and target storage curves. The results indicate that the proposed operating rules are suitable for the complex system. The storage allocation rule based on target storage curves shows an improved performance with regard to system storage distribution.

Keywords reservoir operation   joint operating rules   simulation-optimization model   improved particle swarm optimization  
Received 2014-01-13 Revised 2014-08-05 Online: 2014-10-27 
DOI: 10.3882/j.issn.1674-2370.2014.04.008
Fund:

This work was supported by the National Natural Science Foundation of China (Grants No. 51339004 and 71171151).

Corresponding Authors: Tie-song HU
Email: tshu@whu.edu.cn
About author:

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