Optimization of Wind Farm Layout by Repetitive Rearrangement

Securing energy supply and expanding fuel sources is one of the primary objectives of the energy system for most nations. Due to environmental change, wind energy is getting progressively significant as a strategy for CO2-free energy generation. The renewable energy field has many challenges, including wind farm layout optimization. The wind farm layout optimization is a complex problem in terms of computation expense. The factor affecting the efficiency of turbines is the wake effect which results in a diminution of kinetic downstream caused by the impact of the turbines on each other. Many researchers have applied various algorithms like Genetic Algorithm, L-SHADE, Greedy, Bionic, and Reinforcement Learning to achieve the maximum power output from wind farms. This paper de-scribes and presents an overview of the wind farm layout design problem, proposes a new algorithm for optimization by repetitive rearrangement, and evaluates the performance of the proposed algorithm by the python-based implementation for solving this NP-hard optimization problem. The outcomes obtained reveal lower computational costs to get better-optimized results using the proposed algorithm, thus outperforming in terms of simplicity and computation time to maximize annual energy production from the wind farm.