Optimal Configuration of Capacity for Hybrid Energy Storage Based on ISDPC–K-means Clustering and Cluster Division

To solve the problem of a mismatch between the available renewable energy and the demand for load that is caused by access to a high ratio of renewable energy, this paper proposes a method to optimally configure the system's capacity for a hybrid energy storage system (HESS) based on a comprehensive system of indices for cluster division. We first design a clustering algorithm by combining the improved self-adaptive density peak clustering (ISDPC) algorithm with the K-means clustering algorithm to classify the output curves of renewable energy and generate typical scenarios. We then propose the system indices for cluster division that is composed of modularity, active balance of power, and net fluctuations in load. This system is used for cluster division of distribution network. The net load in the cluster is then decomposed into high frequency components and low frequency components by using variational mode decomposition to obtain reference values of power for power-based and energy-based storage. Finally, we establish an optimal model to configure the capacity of the HESS with the objective of minimizing the annual cost of its configuration and net fluctuations in load, and use the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm to solve it. Simulations of the modified IEEE 33-bus distribution system were used to verify the effectiveness and feasibility of the proposed method.