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Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation

Author

Listed:
  • Mohammad Reza Sharifi

    (Shahid Chamran University of Ahvaz)

  • Saeid Akbarifard

    (Shahid Chamran University of Ahvaz)

  • Kourosh Qaderi

    (Shahid Bahonar University of Kerman)

  • Mohamad Reza Madadi

    (University of Jiroft)

Abstract

Optimal operation of cascade hydropower reservoirs is a complex high-dimensional engineering problem. Developing an appropriate model to solve such problems requires an efficient search method proportional to the dimensions of the problem. Accordingly, this research employed the new fitness-distance-balance (FDB) selection method in the moth swarm algorithm (MSA) to achieve promoted FDB-MSA with a high performance in solving complex large-scale problems. To ensure the efficiency of the developed algorithm, five benchmark functions of Shekel, Six-Hump Camel, McCormick, Goldstein-Price and Rosenbrock were used. Then, the FDB-MSA was used for optimization of hydropower generation of a real five-reservoir system along Karun River at Iran. This is the largest cascade reservoir system in Iran, which supplies more than 90% of the country’s hydropower demand. The results of the developed algorithm were compared with those of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. It was found that the FDB-MSA could successfully increase the hydropower generation by 59.5% (6724 GW) compared to the actual generation of energy over a 180-months operational period. The corresponding values for PSO and GA algorithms were 54.3% and 9.2% respectively. In addition, the results revealed the superiority of FDB-MSA to GA and PSO, so that, it demonstrated the smallest difference (3.41%) between nominal and optimal power generation compared to the PSO (6.58%) and GA (33.89%).

Suggested Citation

  • Mohammad Reza Sharifi & Saeid Akbarifard & Kourosh Qaderi & Mohamad Reza Madadi, 2021. "Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 385-406, January.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:1:d:10.1007_s11269-020-02745-8
    DOI: 10.1007/s11269-020-02745-8
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    References listed on IDEAS

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    1. Shen, Jianjian & Cheng, Chuntian & Wang, Sen & Yuan, Xiaoye & Sun, Lifei & Zhang, Jun, 2020. "Multiobjective optimal operations for an interprovincial hydropower system considering peak-shaving demands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    2. Saad Dahmani & Djilali Yebdri, 2020. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Reservoir Operation Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4545-4560, December.
    3. Mohamad Reza Madadi & Saeid Akbarifard & Kourosh Qaderi, 2020. "Performance Evaluation of Improved Symbiotic Organism Search Algorithm for Estimation of Solute Transport in Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1453-1464, March.
    4. 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.
    5. Yucesan, Melih & Kahraman, Gökhan, 2019. "Risk evaluation and prevention in hydropower plant operations: A model based on Pythagorean fuzzy AHP," Energy Policy, Elsevier, vol. 126(C), pages 343-351.
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    2. Zhennan Liu & Qiongfang Li & Jingnan Zhou & Weiguo Jiao & Xiaoyu Wang, 2021. "Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2921-2940, July.
    3. Hamid Reza Yavari & Amir Robati, 2021. "Developing Water Cycle Algorithm for Optimal Operation in Multi-reservoirs Hydrologic System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2281-2303, June.
    4. Yanpin Li & Huiliang Wang & Zichao Zhang & Huawei Li & Xiaoli Wang & Qifan Zhang & Tong Zhou & Peng Zhang & Fengxiang Chang, 2023. "Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life," Energies, MDPI, vol. 16(13), pages 1-17, June.

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