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Dynamic Feasible Region Genetic Algorithm for Optimal Operation of a Multi-Reservoir System

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  • Bin Xu

    (College of Hydrology and Water Resources, Hohai University, No.1, Xikang Road, Nanjing 210098, China)

  • Ping-An Zhong

    (College of Hydrology and Water Resources, Hohai University, No.1, Xikang Road, Nanjing 210098, China
    National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, No.1, Xikang Road, Nanjing 210098, China)

  • Xinyu Wan

    (College of Hydrology and Water Resources, Hohai University, No.1, Xikang Road, Nanjing 210098, China)

  • Weiguo Zhang

    (College of Hydrology and Water Resources, Hohai University, No.1, Xikang Road, Nanjing 210098, China)

  • Xuan Chen

    (College of Hydrology and Water Resources, Hohai University, No.1, Xikang Road, Nanjing 210098, China)

Abstract

Seeking the optimal strategy of a multi-reservoir system is an important approach to develop hydropower energy, in which the Genetic Algorithm (GA) is commonly used as an effective tool. However, when the traditional GA is applied in solving the problem, the constraints of water balance equation, hydraulic continuity relationship and power system load demand might be violated by the crossover and mutation operator, which decreases the efficiency of the algorithm in searching for a feasible region or even leads to a convergence on an infeasible chromosome within the expected generations. A modified GA taking stochastic operators within the feasible region of variables is proposed. When determining the feasible region of constraints, the progressive optimal approach is applied to transform constraints imposed on reservoirs into a singular-reservoir constraint, and a joint solution with consideration of adjacent periods at crossover or mutation points is used to turn the singular-reservoir constraints into singular variable constraints. Some statistic indexes are suggested to evaluate the performances of the algorithms. The experimental results show that compared to GA adopting a penalty function or pair-wise comparison in constraint handling, the proposed modified GA improves the refinement of the quality of a solution in a more efficient and robust way.

Suggested Citation

  • Bin Xu & Ping-An Zhong & Xinyu Wan & Weiguo Zhang & Xuan Chen, 2012. "Dynamic Feasible Region Genetic Algorithm for Optimal Operation of a Multi-Reservoir System," Energies, MDPI, vol. 5(8), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:8:p:2894-2910:d:19322
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    References listed on IDEAS

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    Cited by:

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    6. Perica Ilak & Slavko Krajcar & Ivan Rajšl & Marko Delimar, 2014. "Pricing Energy and Ancillary Services in a Day-Ahead Market for a Price-Taker Hydro Generating Company Using a Risk-Constrained Approach," Energies, MDPI, vol. 7(4), pages 1-26, April.
    7. Yi Liu & Zhiqiang Jiang & Zhongkai Feng & Yuyun Chen & Hairong Zhang & Ping Chen, 2019. "Optimization of Energy Storage Operation Chart of Cascade Reservoirs with Multi-Year Regulating Reservoir," Energies, MDPI, vol. 12(20), pages 1-20, October.
    8. Shuai Liu & Zhong-Kai Feng & Wen-Jing Niu & Hai-Rong Zhang & Zhen-Guo Song, 2019. "Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation," Energies, MDPI, vol. 12(11), pages 1-24, June.

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