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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. 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).

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