A two-level approach for three-dimensional micro-siting optimization of large-scale wind farms. (1st January 2020) - Song, Mengxuan; Chen, Kai; Wang, Jun
Abstract: For three-dimensional micro-siting of large-scale wind farms, it is difficult for the traditional once-and-for-all approach to search for optimal solutions when tuning all the turbine locations and heights simultaneously. In this paper, a two-level optimization approach is presented for three-dimensional micro-siting optimization of large-scale wind farms. The proposed approach uses a hierarchical structure, where the wind farm is considered as a compound that consists of several blocks with identical sizes. A multi-objective algorithm is used to optimize one block, producing a set of block candidates. Then the compound-level algorithm optimizes the whole wind farm layout by searching among all possible combinations of the block candidates. The proposed approach is tested on 24 cases with different choices and combinations of objective, wind scenario and number of turbines. The results show that the proposed method can significantly reduce the difficulty of searching for the optimal solution, and demonstrates noticeable improvements than the traditional one-level approach. Highlights: Dimension of solution space is reduced by performing PCA on Pareto front. Hierarchical approach greatly reduces the difficulty of optimization. Multi-objective algorithm obtains solutions with more power and less cost.