Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy

The micro-siting of wind farms has recently attracted much attention due to the booming development of wind energy. The paper aims to maximize the electrical power extracted from a wind farm while satisfying the required distance between turbines for operation safety. The micro-siting problem is by nature a constrained optimization problem, in which the coupling of wake effects is strong and the number of position constraints between turbines is large. An improved Gaussian particle swarm optimization algorithm is proposed to optimize the positions of turbines in the continuous space. To prevent the premature of the algorithm, a local search strategy based on differential evolution is incorporated to search around the promising region achieved by the particle swarm optimization. A simple feasibility-based method is employed to compare the performance of different schemes. Comprehensive simulation results demonstrate that the micro-siting schemes obtained by the proposed algorithm increase the power generation of the wind farm. Moreover, the execution time of the algorithm is significantly reduced, which is important especially for large-scale wind farms.