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Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization

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  • Yu, Xiaobing
  • Lu, Yangchen

Abstract

Wind farm layout optimization is a challenging issue which demands to discover some trade-off solutions considering various criteria, such as the power generated and the cost of the farm. Due to the complexity of the problem, we developed a reinforcement learning-based multi-objective differential evolution (RLMODE) algorithm to address the issue. In the developed algorithm, RL technique is applied to coordinate the parameter of DE algorithm, which can balance the local and global search. A tournament-based mutation operator is used to accelerate the convergence of the RLMODE algorithm. We tested the performance of the proposed RLMODE in two wind scenarios. The spread and spacing indicators of the algorithm are the best; the power generated by the solution from the RLMODE algorithm is the most when compared with some representative optimization algorithms and existing methods.

Suggested Citation

  • Yu, Xiaobing & Lu, Yangchen, 2023. "Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223026944
    DOI: 10.1016/j.energy.2023.129300
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    References listed on IDEAS

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    1. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    2. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    3. Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    4. Sun, Haiying & Yang, Hongxing, 2023. "Wind farm layout and hub height optimization with a novel wake model," Applied Energy, Elsevier, vol. 348(C).
    5. Cazzaro, Davide & Koza, David Franz & Pisinger, David, 2023. "Combined layout and cable optimization of offshore wind farms," European Journal of Operational Research, Elsevier, vol. 311(1), pages 301-315.
    6. Li, Wenwen & Özcan, Ender & John, Robert, 2017. "Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation," Renewable Energy, Elsevier, vol. 105(C), pages 473-482.
    7. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).
    8. Dong, Hongyang & Zhang, Jincheng & Zhao, Xiaowei, 2021. "Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations," Applied Energy, Elsevier, vol. 292(C).
    9. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    10. Reddy, Sohail R., 2020. "Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization," Applied Energy, Elsevier, vol. 269(C).
    11. Rodrigues, S. & Bauer, P. & Bosman, Peter A.N., 2016. "Multi-objective optimization of wind farm layouts – Complexity, constraint handling and scalability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 587-609.
    12. Reddy, Sohail R., 2021. "An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization," Renewable Energy, Elsevier, vol. 165(P1), pages 162-173.
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    Cited by:

    1. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    2. Kim, Taewan & Song, Jeonghwan & You, Donghyun, 2024. "Optimization of a wind farm layout to mitigate the wind power intermittency," Applied Energy, Elsevier, vol. 367(C).

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