Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach

被引:120
|
作者
Ostadrahimi, Leila [1 ]
Marino, Miguel A. [1 ]
Afshar, Abbas [2 ]
机构
[1] Univ Calif Davis, Dept Civil & Environm Engn, Davis, CA 95616 USA
[2] Iran Univ Sci & Technol, Sch Civil & Environm Engn, Tehran, Iran
关键词
Reservoir operation; Multi swarm; PSO; Simulation-optimization; Operating rule; ALGORITHM; POLICIES; SYSTEMS;
D O I
10.1007/s11269-011-9924-9
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Reservoir operation rules are intended to help an operator so that water releases and storage capacities are in the best interests of the system objectives. In multi-reservoir systems, a large number of feasible operation policies may exist. System engineering and optimization techniques can assist in identifying the most desirable of those feasible operation policies. This paper presents and tests a set of operation rules for a multi-reservoir system, employing a multi-swarm version of particle swarm optimization (MSPSO) in connection with the well-known HEC-ResPRM simulation model in a parameterization-simulation-optimization (parameterization SO) approach. To improve the performance of the standard particle swarm optimization algorithm, this paper incorporates a new strategic mechanism called multi-swarm into the algorithm. Parameters of the rule are estimated by employing a parameterization-simulation-optimization approach, in which a full-scale simulation model evaluates the objective function value for each trial set of parameter values proposed with an efficient version of the particle swarm optimization algorithm. The usefulness of the MSPSO in developing reservoir operation policies is examined by using the existing three-reservoir system of Mica, Libby, and Grand Coulee as part of the Columbia River Basin development. Results of the rule-based reservoir operation are compared with those of HEC-ResPRM. It is shown that the real-time operation of the three reservoir system with the proposed approach may significantly outperform the common implicit stochastic optimization approach.
引用
收藏
页码:407 / 427
页数:21
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