A shared trading method for distributed energy storage aggregators based on an improved particle swarm algorithm

To address the problems of high cost, low utilization rate, and single operation mode that exist in the user-side distributed energy storage system. This paper proposes an innovative energy storage aggregator (ESA) self-owned and shared electricity trading method. The method establishes a day-ahead optimization model for ESA control of storage charging and discharging through time-sharing tariffs and user load forecasting, achieving the goal of maximizing ESA's overall efficiency. Meanwhile, an optimization model for real-time electricity trading is constructed to enhance the utilization rate of the stored energy equipment and optimize their operational benefits by considering the dynamic demand in the electricity market. To verify the feasibility and validity of the proposal model, this paper adopts the particle swarm algorithm which is effective in solving this kind of problem. Study results show that the method realizes the efficient management of the energy storage system, increases the sharing and utilization of electricity resources, and improves the economy and flexibility of the storage system.