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Optimal Operation of Multi-reservoir System for Hydropower Production Using Particle Swarm Optimization Algorithm

Author

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  • Yousif H. Al-Aqeeli

    (University of Mosul)

  • Omar M. A Mahmood Agha

    (University of Mosul)

Abstract

The determination of optimal operation rules for water storage systems will provide a good perception on the ability of these systems to achieve their objective functions. This study aims to identify optimal operation policies by maximizing the annual hydropower generation of a multi-reservoir system that consists of two reservoirs with different functions in flood risk management. These reservoirs, namely, Mosul and Badush, are located in the Tigris River, Northern Iraq. The Mosul Dam was constructed to protect the cities located downstream, and the Badush Dam is building to absorb flood waves in case the Mosul Dam collapses. The particle swarm optimization model for a single reservoir (PSOS) was formulated to determine optimal operation policies during real operation time to maximize annual hydroelectric generation. PSOS was approved during these operations and developed to specify ideal operation rules for a multi-reservoir system (PSOM), which consists of two reservoirs, through three modes of annual inflows. Results of PSOS indicated its superiority during real-time operation. The annual hydropower generation was achieved by the optimal operation rules of PSOM during the three styles of inflows. These optimal policies will provide good insights into the potential of this multi-reservoir system in supplying the national electricity network with hydropower energy, which is considered environmentally friendly, in addition to achieving the original goals of its construction.

Suggested Citation

  • Yousif H. Al-Aqeeli & Omar M. A Mahmood Agha, 2020. "Optimal Operation of Multi-reservoir System for Hydropower Production Using Particle Swarm Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3099-3112, August.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:10:d:10.1007_s11269-020-02583-8
    DOI: 10.1007/s11269-020-02583-8
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    References listed on IDEAS

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    1. Yi-min Wang & Jian-xia Chang & Qiang Huang, 2010. "Simulation with RBF Neural Network Model for Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2597-2610, September.
    2. Lejun Ma & Huan Wang & Baohong Lu & Changjun Qi, 2018. "Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    3. Leila Ostadrahimi & Miguel Mariño & Abbas Afshar, 2012. "Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 407-427, January.
    4. Xiang Yu & Hui Sun & Hui Wang & Zuhan Liu & Jia Zhao & Tianhui Zhou & Hui Qin, 2016. "Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization," Energies, MDPI, vol. 9(6), pages 1-18, June.
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    Cited by:

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    2. Rahim Zahedi & Reza Eskandarpanah & Mohammadhossein Akbari & Nima Rezaei & Paniz Mazloumin & Omid Noudeh Farahani, 2022. "Development of a New Simulation Model for the Reservoir Hydropower Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2241-2256, May.
    3. Favaro, Pietro & Dolányi, Mihály & Vallée, François & Toubeau, Jean-François, 2024. "Neural network informed day-ahead scheduling of pumped hydro energy storage," Energy, Elsevier, vol. 289(C).
    4. Inkyung Min & Nakyung Lee & Sanha Kim & Yelim Bang & Juyeon Jang & Kichul Jung & Daeryong Park, 2024. "An Improved Aggregation–Decomposition Optimization Approach for Ecological Flow Supply in Parallel Reservoir Systems," Sustainability, MDPI, vol. 16(17), pages 1-22, August.

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