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Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm

Author

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  • Mohammad Azizipour

    (University of California)

  • Vahid Ghalenoei

    (Iran University of Science and Technology)

  • M. H. Afshar

    (Iran University of Science and Technology)

  • S. S. Solis

    (University of California)

Abstract

The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems.

Suggested Citation

  • Mohammad Azizipour & Vahid Ghalenoei & M. H. Afshar & S. S. Solis, 2016. "Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3995-4009, September.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:11:d:10.1007_s11269-016-1407-6
    DOI: 10.1007/s11269-016-1407-6
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    6. Chengjun Wu & Guohua Fang & Tao Liao & Xianfeng Huang & Bo Qu, 2020. "Integrated Software Development and Case Studies for Optimal Operation of Cascade Reservoir within the Environmental Flow Constraints," Sustainability, MDPI, vol. 12(10), pages 1-16, May.
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    10. Avesani, Diego & Zanfei, Ariele & Di Marco, Nicola & Galletti, Andrea & Ravazzolo, Francesco & Righetti, Maurizio & Majone, Bruno, 2022. "Short-term hydropower optimization driven by innovative time-adapting econometric model," Applied Energy, Elsevier, vol. 310(C).
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