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Wind Farm Layout Optimization Using Multiobjective Modified Electric Charged Particles Optimization Algorithm Based on Game Theory Indexing in Real Onshore Area

Author

Listed:
  • Taufal Hidayat

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Makbul A. M. Ramli

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Apri Zulmi Hardi

    (Department of Urban and Regional Planning, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Houssem R. E. H. Bouchekara

    (Department of Electrical Engineering, University of Hafr Al Batin, Hafar Al-Batin 31911, Saudi Arabia)

  • Ahmad H. Milyani

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Center of Excellent in Intelligent Engineering System (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Designing onshore wind farms presents unique challenges related to interactions between terrain and landscape characteristics. This research focuses on optimizing the layout of onshore wind farms while considering the effect of the terrain and land characteristics. Three real onshore site areas in South Sulawesi, Indonesia, are selected for wind farm design and optimization. A novel optimization algorithm, the Multiobjective Modified Electric Charged Particles Optimization (MOMECPO), is introduced to minimize both the Levelized Cost of Electricity (LCOE) and noise levels. This algorithm employs a new game theory-based indexing method to effectively sort the Pareto solution set. The results show that the proposed algorithm enhances the exploration and exploitation capabilities of the solutions obtained. Our optimal solutions demonstrate that MOECPO achieves LCOE values of 6.78, 7.73, and 5.56 US cents/kWh for Sites 1, 2, and 3, respectively. Correspondingly, noise levels are recorded at 53.71 dBA, 52.53 dBA, and 55.25 dBA for the same sites. These values outperform seven other comparative algorithms, with NSGA achieving the closest performance among them, yielding LCOE values of 6.865, 7.815, and 5.579 US cents/kWh, and noise levels of 53.858, 52.556, and 55.197 dBA for Sites 1, 2, and 3, respectively. As for the terrain effect, our findings reveal that sites with complex terrains tend to have higher AEP and lower LCOE due to the steeper slopes. However, this site also experiences increased noise levels because of the higher energy production.

Suggested Citation

  • Taufal Hidayat & Makbul A. M. Ramli & Apri Zulmi Hardi & Houssem R. E. H. Bouchekara & Ahmad H. Milyani, 2024. "Wind Farm Layout Optimization Using Multiobjective Modified Electric Charged Particles Optimization Algorithm Based on Game Theory Indexing in Real Onshore Area," Sustainability, MDPI, vol. 16(23), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10222-:d:1526909
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    References listed on IDEAS

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    1. Tingey, Eric B. & Ning, Andrew, 2017. "Trading off sound pressure level and average power production for wind farm layout optimization," Renewable Energy, Elsevier, vol. 114(PB), pages 547-555.
    2. Takanori Uchida & Susumu Takakuwa, 2019. "A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain," Energies, MDPI, vol. 12(10), pages 1-19, May.
    3. Wu, Xiawei & Hu, Weihao & Huang, Qi & Chen, Cong & Jacobson, Mark Z. & Chen, Zhe, 2020. "Optimizing the layout of onshore wind farms to minimize noise," Applied Energy, Elsevier, vol. 267(C).
    4. Anabela Botelho & Pedro Arezes & Carlos Bernardo & Hernâni Dias & Lígia M. Costa Pinto, 2017. "Effect of Wind Farm Noise on Local Residents’ Decision to Adopt Mitigation Measures," IJERPH, MDPI, vol. 14(7), pages 1-20, July.
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