Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization

TL;DR

This paper introduces a reinforcement learning-enhanced genetic algorithm (RLGA) that dynamically optimizes wind farm layouts, outperforming traditional genetic algorithms especially in complex scenarios, by improving efficiency and convergence.

Contribution

The paper presents a novel RLGA method that adaptively tunes parameters during genetic algorithm execution, enhancing performance in complex wind farm layout optimization problems.

Findings

RLGA achieves similar results to GA on simple layouts.

RLGA outperforms GA on complex layouts like sunflower and unstructured.

RLGA is about three times more efficient than GA for large, complex wind farm problems.

Abstract

A reinforcement learning-enhanced genetic algorithm (RLGA) is proposed for wind farm layout optimization (WFLO) problems. While genetic algorithms (GAs) are among the most effective and accessible methods for WFLO, their performance and convergence are highly sensitive to parameter selections. To address the issue, reinforcement learning (RL) is introduced to dynamically select optimal parameters throughout the GA process. To illustrate the accuracy and efficiency of the proposed RLGA, we evaluate the WFLO problem for four layouts (aligned, staggered, sunflower, and unstructured) under unidirectional uniform wind, comparing the results with those from the GA. RLGA achieves similar results to GA for aligned and staggered layouts and outperforms GA for sunflower and unstructured layouts, demonstrating its efficiency. The sunflower and unstructured layouts' complexity highlights RLGA's robustness and efficiency in tackling complex problems. To further validate its capabilities, we investigate larger wind farms with varying turbine placements ($\Delta x = \Delta y = 5D$ and 2$D$, where $D$ is the wind turbine diameter) under three wind conditions: unidirectional, omnidirectional, and non-uniform, presenting greater challenges. The proposed RLGA is about three times more efficient than GA, especially for complex problems. This improvement stems from RL's ability to adjust parameters, avoiding local optima and accelerating convergence.