Probabilistic Energy Flow Algorithm for Integrated Energy Systems Based on Fuzzy C-means Clustering

Abstract

Integrated energy systems is an effective way to achieve carbon neutrality by improving the utilization of renewable energy through the synergistic complementation of multiple energy sources. At present, hard clustering algorithm such as K-means clustering is usually used for clustering in the probabilistic flow calculation of power system, but they will bring some error in the multi-energy coupling scenarios with multiple uncertain variables. Therefore, in this paper, we consider the uncertainty of renewable energy and multiple energy loads to model and apply the fuzzy C-means clustering algorithm in probabilistic energy flow calculation for the integrated energy systems of electricity, heat and gas. First, establish a hybrid energy flow model of the integrated energy systems considering the uncertainty and relevance of renewable energy and multiple energy loads. Then the cumulant method is used to solve the probabilistic energy flow and the fuzzy C-means clustering algorithm is used to cluster the scenarios and reduce the linearization error. Finally, the simulation analysis of the actual case shows the accuracy of the probabilistic energy flow calculation applying the fuzzy C-means clustering algorithm.