Two-stage optimization configuration of shared energy storage for multi-distributed photovoltaic clusters in rural distribution networks considering self-consumption and self-sufficiency

The integration of energy storage (ES) systems with distributed photovoltaic (DPV) generation in rural Chinese distribution networks enhances self-consumption while mitigating grid congestion. However, the geographically dispersed nature of rural DPV deployment leads to suboptimal storage utilization when configuring ES for individual village-level DPV clusters, primarily due to the absence of inter-cluster energy exchange. This operational inefficiency significantly escalates both initial investment and maintenance costs. In this paper, considering the complementarity between outputs of DPV clusters and residential loads in different villages, a cooperative operation strategy for multi-DPV clusters and shared energy storage (SES) is proposed with the goal of improving the self-consumption and self-sufficiency. Then, a comprehensive life-cycle cost-income analysis framework and a two-stage SES optimization configuration model is developed. The proposed model is solved by the particle swarm algorithm with improved adaptive inertia weights (APSO). A rural DPV demonstration zone in northern China serves as the case study, where multiple scenarios incorporating various ES configurations and demand response (DR) implementations are designed. Comparative analysis reveals that SES outperforms distributed energy storage (DES), boosting PV self-consumption by 2.44 %, increasing power self-sufficiency by 2.26 %, and lowering levelized annual costs per rural household by 2.54 %. When integrated with DR, these benefits increase to 3.46 %, 3.20 %, and 3.72 % respectively. The research outcomes provide useful reference for investment planning and coordinated operation of multi-DPV clusters and shared storage systems, facilitating sustainable development of DPV in rural areas.