Distributed parallel optimal operation for shared energy storage system - multiple park integrated energy system based on ADMM

Integrating a shared energy storage system (SESS) into multiple park integrated energy systems (MPIES) enables flexible capacity selection for each park, considerably enhancing the utilization rate of energy storage equipment. Reasonable capacity configuration and operational strategies for the SESS can harness the advantages of differences and complementarities in the source–load among the parks, thereby meeting various needs of all parties while improving the operational economics. To address this issue, this study proposes a distributed cooperative optimization model for SESS-MPIES that accounts for seasonal variations. First, the proposed model optimizes the SESS's annual profit and minimizes the annual energy costs for each park by using an asymmetric Nash bargaining model, which prioritizes collective benefits while ensuring fairness for individual stakeholders. A distributed parallel optimization framework is then introduced, decomposing the problem into two subproblems using the alternating direction method of multipliers. Variable information within each park is updated in parallel, and an adaptive step-size mechanism improves the solution process's efficiency. Finally, simulations are performed on an integrated energy system comprising three parks and a SESS using MATLAB. The results demonstrate that the proposed strategy reduces the overall annual operating cost of the system by 27 %, with the lifecycle profit rate of the SESS reaching 13.6 %. Furthermore, the proposed parallel algorithm notably improves computation speed.