Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy

Abstract

Distributed joint complementary wind generation is a powerful way to reduce the variability and improve the penetration of wind power. But distributed wind farm siting is a complex problem of nonlinear and global combinatorial optimization. Various correlation coefficients were used to measure the complementarity of renewable energy, which were not intuitive and accurate in former studies, and traditional siting methods are based on the abundance of wind energy, siting results cannot fully utilize the complementary potential of distributed wind energy. In this study, intelligent optimization algorithm is adopted to select the location of distributed wind farm in China mainland from the perspective of utilizing the complementarity of wind energy. The result showed that the existing wind power bases have obvious complementary superiority, combined power generation can improve availability from about 57.61% to 66.93%, reduce the long/short term variability confidence by about 40%. Siting result indicated that central and eastern Inner Mongolia, Xinjiang and Hexi Corridor are still key regions for the future wind farms construction. This study provides more direct evidence of the complementarity of existing wind power bases, and the proposed siting methodology can be applied in different regions worldwide.