A Novel Approach for Wind Farm Micro-Siting in Complex Terrain Based on an Improved Genetic Algorithm

This study presents a novel approach for wind farm micro-siting in complex terrain based on an improved Genetic Algorithm (GA). The proposed approach is used for optimizing the layout of wind turbines in a real terrain in Changsha, China. Firstly, a combination of computational fluid dynamics (CFD) simulations and meteorological measurements is applied to determine the distribution of wind potential energy and wind acceleration ratio. By comparing with the wind tunnel tests, results of the CFD simulations are validated. Secondly, an improved GA for optimizing the layout of wind turbines is adopted, which takes into account the wind power density and wind acceleration ratio. Calculation of the population’s objective function is programmed uniquely and parallelized, and a variable mutation rate is utilized. Finally, optimal layout of the wind turbines is searched by the improved GA. Performance of the proposed approach is investigated and is compared with the Greedy algorithm and the original GA. Three cases with various wake models and objective functions are analyzed. The results indicate that the proposed approach independent of wake models and objective functions is more effective in terms of offering a reasonable wind turbine layout plan than above two previously mentioned methods