分布式光伏集群功率短期预测的空间互补特性初探OA北大核心CSTPCD

With the rapid growth of distributed photovoltaic(PV)installed capacity,the impact of power prediction for distributed PV clusters on grid dispatch is becoming increasingly significant.There are three technical routes for power prediction of distributed PV clusters,i.e.,accumulation before prediction,prediction before accumulation and clustering before prediction.Through experiments on a dataset of over 600 distributed PV sites,the spatial complementarity is found in the power prediction for distributed PV clusters,whereby the prediction errors of the three cluster prediction routes are lower than the average prediction error of a single site.To explore the generation mechanism and influencing factors of the spatial complementarity,this paper first categorizes it into two types of spatial complementarity characteristics,namely power curve complementarity and prediction error complementarity,based on the mechanism analysis.Secondly,the concept of spatial complementarity coefficient is proposed to quantitatively evaluate the complementary effect.Finally,the effects of cluster scale,distribution range,weather type and number of clusters on the spatial complementarity characteristics are explored.The results show that the two types of spatial complementarity characteristics have significant effects on improving the short-term power prediction accuracy of distributed PV clusters,with prediction error complementarity superior to power curve complementarity.The research results can provide a basis for the division of distributed PV clusters and contribute to achieving more efficient and accurate power prediction.