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

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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%).

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Data Availability

All data, models, or code generated or used during the study are available from the corresponding author by request.

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Acknowledgements

We are grateful to the Research Council of Shahid Chamran University of Ahvaz for financial support (GN: SCU.WH99.26878). Also, we acknowledge the Khuzestan Water and Power Authority.

Funding

No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Mohammad Reza Sharifi

  2. Candidate of Water Resources Engineering, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Saeid Akbarifard

  3. Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

    Kourosh Qaderi

  4. Department of Water Engineering, Faculty of Agriculture, University of Jiroft, Jiroft, Iran

    Mohamad Reza Madadi

Authors
  1. Mohammad Reza Sharifi
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  2. Saeid Akbarifard
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  3. Kourosh Qaderi
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  4. Mohamad Reza Madadi
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Contributions

Saeid Akbarifard: Methodology, Writing, Original draft preparation, Data analysis. Mohamad Reza Sharifi: Conceptualization, Data collection, Supervision. Kourosh Qaderi: Visualization, Review, Supervision. Mohamad Reza Madadi: Review, English editing, Revision preparation.

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Correspondence to Saeid Akbarifard.

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Sharifi, M.R., Akbarifard, S., Qaderi, K. et al. Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation. Water Resour Manage 35, 385–406 (2021). https://doi.org/10.1007/s11269-020-02745-8

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  • DOI: https://doi.org/10.1007/s11269-020-02745-8

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