Joint Distributed Energy Storage and Distribution Network Planning Considering Short‐Term and Long‐Term Uncertainties

ABSTRACT

Distributed energy storage has been widely deployed to enhance operational flexibility within distribution systems amid the ongoing energy transition. However, the multi‐timescale uncertainties represent a significant challenge to the optimal planning of energy storage and distribution network. This paper proposes a consecutive long‐term joint planning model of distributed energy storage and distribution network that employs chanceconstrained method to address short‐term uncertainties, such as distributed energy resource output and load demand, and long‐term uncertainties, including storage cost and distributed energy resource capacity. The model integrates network reconfiguration to enhance flexibility and determines optimal energy storage construction time, construction location, and rated capacity. The effectiveness of the model and the benefits of the chance‐constrained method are validated through an enhanced IEEE 33‐bus system. Furthermore, the model's capability to address two extreme scenarios of high penetration of distributed energy resources and rapid growth of electric vehicle load is illustrated using real‐world data from a 63‐bus distribution network in China. The proposed planning model is solved efficiently using the Gurobi optimizer. The results show that the proposed approach reduces planning costs by 5.55% compared to robust planning and improves the extreme‐scenario pass rate by 19.20% relative to deterministic planning.