Comprehensive energy collaborative planning method based on the spatial correlation of wind and solar

Abstract The stability and efficiency of the energy system’s use have been greatly impacted by the large percentage of new energy access. Therefore, an integrated energy collaborative planning technique based on the geographical correlation of solar and wind is presented to ensure the stability of the energy system and enhance energy consumption. This method first uses an improved particle swarm optimization k-means algorithm to cluster the spatial correlations of wind and solar scenarios to obtain typical scenarios as inputs for constructing an energy planning model. Based on this, a comprehensive energy system planning model is constructed. Finally, by using the target cascade analysis method to solve the model, a comprehensive collaborative energy planning scheme can be obtained. The experimental results indicated that the improved particle swarm-K-means algorithm was able to generate 50 typical scenarios of wind power and photovoltaic. Scenarios 1–48 were the clustering centers, while scenarios 49 and 50 were the extreme scenarios of wind power output. Compared to the energy planning models based on cloud model-fuzzy c-means clustering and fast search and clustering of density peak values, the cost of the proposed integrated energy planning model was reduced by 2.58% and 14.55% respectively. Meanwhile, in the energy planning method proposed by the study, the exchanged power of transmission and distribution grids was up to 8.1 MW. The exchanged power of distribution and gas distribution grids was −0.8 MW, and the energy utilization efficiency was high. The aforementioned findings demonstrate that the study’s integrated energy planning model can successfully lower energy planning expenses while guaranteeing the integrated energy system’s overall benefit.