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

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 e...

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Veröffentlicht in:Energy (Oxford) Jg. 284; S. 129300
Hauptverfasser: Yu, Xiaobing, Lu, Yangchen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2023
Schlagworte:
algorithms
Differential evolution
energy
farms
Multi-objective
mutation
Reinforcement learning
wind
Wind energy
wind farms
Differential evolution
Wind energy
Multi-objective
Reinforcement learning
ISSN:0360-5442
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Zusammenfassung: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. •The WFLO problem is constructed as a multi-objective item.•The total power generated and the cost of the farm are considered.•A RL-based multi-objective DE (RLMODE) algorithm is established.•The RLMODE is used to optimize the multi-objective WFLO problem.
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ISSN:0360-5442
DOI:10.1016/j.energy.2023.129300