Scheduling optimization of shared energy storage station in industrial park based on reputation factor

Distributed photovoltaics (PVs) installed in industrial parks are important measures for reducing carbon emis-sions. However, the consumption level of PV power generation in different industries varies significantly, and it is often difficult to consume 100% of the PV power generation. The shared energy storage station (SESS) can improve the consumption level of PV power generation. In this study, a reputation factor pricing strategy for an SESS was proposed and a mixed integer linear programming (MILP) model with the goal of maximizing the daily net income of the SESS was established. The optimal energy scheduling results of different pricing strategies were compared using this MILP model. Financial evaluation indexes were calculated for users and SESS in various scenarios. Regardless of the pricing strategy implemented by SESS, the financial evaluation indexes for users were consistently better with SESS than without it. This highlighted the benefits of SESS in reducing energy costs for users. The reputation factor pricing strategy scenario increased the NPV of the SESS by 76% compared with the same discount pricing strategy scenario. Finally, a sensitivity analysis found that the economic advantages of the reputation factor pricing strategy improved with increased difference in the utility grid peak-valley tariff.