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Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm

Author

Listed:
  • Aida Tayebiyan

    (University Putra Malaysia)

  • Thamer Ahmed Mohammed Ali

    (University Putra Malaysia)

  • Abdul Halim Ghazali

    (University Putra Malaysia)

  • M. A. Malek

    (University Tenaga Nasional)

Abstract

The efficient utilization of hydropower resources play an important role in the economic sector of power systems, where the hydroelectric plants constitute a significant portion of the installed capacity. Determination of daily optimal hydroelectric generation scheduling is a crucial task in water resource management. By utilizing the limited water resource, the purpose of hydroelectric generation scheduling is to specify the amount of water releases from a reservoir in order to produce maximum power, while the various physical and operational constraints are satisfied. Hence, new forms of release policies namely, BSOPHP, CSOPHP, and SHPHP are proposed and tested in this research. These policies could only use in hydropower reservoir systems. Meanwhile, to determine the optimal operation of each policy, real coded genetic algorithm is applied as an optimization technique and maximizing the total power generation over the operational periods is chosen as an objective function. The developed models have been applied to the Cameron Highland hydropower system, Malaysia. The results declared that by using optimal release policies, the output of power generation is increased, while these policies also increase the stability of reservoir system. In order to compare the efficiency of these policies, some reservoir performance indices such as reliability, resilience, vulnerability, and sustainability are used. The results demonstrated that SHPHP policy had the highest performance among the tested release policies.

Suggested Citation

  • Aida Tayebiyan & Thamer Ahmed Mohammed Ali & Abdul Halim Ghazali & M. A. Malek, 2016. "Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1203-1216, February.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:3:d:10.1007_s11269-015-1221-6
    DOI: 10.1007/s11269-015-1221-6
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    References listed on IDEAS

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    9. Mohammad Ehteram & Ali Najah Ahmed & Ming Fai Chow & Sarmad Dashti Latif & Kwok-wing Chau & Kai Lun Chong & Ahmed El-Shafie, 2023. "Optimal operation of hydropower reservoirs under climate change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10627-10659, October.
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    13. Abdelhady, Hazem U. & Imam, Yehya E. & Shawwash, Ziad & Ghanem, Ashraf, 2021. "Parallelized Bi-level optimization model with continuous search domain for selection of run-of-river hydropower projects," Renewable Energy, Elsevier, vol. 167(C), pages 116-131.
    14. Xinyu Wu & Rui Guo & Xilong Cheng & Chuntian Cheng, 2021. "Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System," Energies, MDPI, vol. 14(3), pages 1-15, January.

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