Two-Layer Optimization Method for Sharing Energy Storage and Energy considering Subjectivity

The high level of integration of distributed generation systems (DGSs), especially distributed wind and solar, significantly affects the flexibility and controllability of the power system. Aggregating local DGSs and shared energy storage systems (ESSs) within an energy community offers an economically and environmentally viable solution. However, the coupling of shared ESSs with the energy community, while considering subjectivity, is often overlooked. Therefore, this study introduces a two-layer optimization framework that enables DGSs to trade energy freely, voluntarily, and independently and to share ESSs within the energy community, considering participants' subjectivity. The upper layer optimizes the size of shared ESSs, while the lower layer, structured as a two-layer model, simulates participant interactions. The numerical case shows that, compared to DGSs operating individually, the shared ESS case indicates that community self-sufficiency and self-consumption rates increase by 16.22% and 21.98%, respectively. Additionally, the annual operating cost is reduced by approximately 27.10%, and CO2 emissions are decreased by about 33.24%. Considering DGS' subjectivity, the self-sufficiency and self-consumption rates are 3.04% lower, and the total operating costs and CO2 emissions are 3.26% and 6.86% higher, respectively.