To accelerate the green transformation of power grids, enhance the accommodation of renewable energy, reduce the operational costs of rural distribution networks, and address voltage stability issues caused by supply-demand fluctuations, this study proposes an optimization method for distributed energy storage systems in rural distribution networks integrated with renewable energy. Initially, the K-means clustering method is employed to analyze 1 year of load and renewable generation data, generating four typical scenarios to represent varying conditions of electricity supply and demand. Based on this analysis, a collaborative optimization model for energy storage and renewable energy-integrated distribution networks is constructed, comprehensively considering operational costs of the rural grid as well as the investment and operational costs of energy storage systems, with the objective of minimizing total operational costs. The optimal locations and capacities of energy storage systems are determined using YALMIP toolbox and the beetle swarm optimization (BSO) algorithm, and the proposed method is validated on a modified IEEE 33-bus system. The results demonstrate that the optimized energy storage planning significantly reduces the operational costs of the rural distribution network, decreases electricity purchasing expenses and curtailment losses of wind and solar energy, and optimizes power flow distribution while enhancing nodal voltage stability. This approach not only improves the economic efficiency and operational performance of rural distribution networks but also provides robust theoretical and technical support for the efficient utilization of renewable energy resources.
Jia et al. (Thu,) studied this question.