A teaching-learning-based optimization algorithm with reinforcement learning to address wind farm layout optimization problem

Xiaobing Yu

doi:10.1016/j.asoc.2023.111135

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A teaching-learning-based optimization algorithm with reinforcement learning to address wind farm layout optimization problem

2024-01-01

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Xiaobing Yu *

Wen Zhang

DOI：10.1016/j.asoc.2023.111135

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Abstract

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As the global demand for renewable energy continues to rise, wind energy has received widespread attention as an eco-friendly energy source. Wind power generation is regarded as one of the key means to reduce carbon emissions and achieve sustainable development. Usually, a mass of turbines works together to produce electricity in a wind farm. However, downstream turbines will inevitably be influenced by the wake generated by upstream turbines, resulting in unused wind energy being lost. To reduce the negative effects of the wake, maximization of wind farm output power, and minimization of wind farm cost, a teaching-learning-based optimization algorithm with reinforcement learning is proposed in this paper. The improvements of the proposed algorithm mainly include the following three points: i) the original serial structure of the algorithm is changed to a parallel structure to accelerate the convergence and improve the efficiency of the algorithm. ii) the parameter F, which is adjusted by RL, is proposed to adjust the selection of the updating phase due to the design of a parallel structure. iii) in the modified learner phase, an individual is added to participate in the update, and a selection probability is proposed to improve the ability of the algorithm to retain the information of superior individuals. To study the performance of the modified algorithm, it was first tested against 10 other advanced algorithms on a benchmark testing suite. They then ran numerical experiments on four hypothetical wind farm cases under two simulated wind conditions. Finally, the superiority of improved algorithm over others and the effectiveness of addressing wind farm layout problem are demonstrated by experimental results.

Keywords:

Teaching-learning-based optimization

Reinforcement learning

Wind farm layout optimization

Q-Learning algorithm

Meta-heuristic algorithm