For large industrial electricity consumers

electricity expenses constitute a significant portion of operational costs. Configuring energy storage systems for peak shaving and valley filling can effectively reduce both energy charges and demand charges. To determine a reasonable monthly declared maximum demand value

this study selects industrial users with different load profiles as research subjects. Considering load forecast uncertainty and scenarios where users share self-built energy storage systems

a two-stage robust optimization model based on Nash bargaining is proposed for shared energy storage. The model decomposes the Nash bargaining problem into two subproblems: a cost minimization problem and a bargaining transfer problem. The cost minimization problem employs a two-stage robust optimization method to determine the monthly declared maximum demand value

ensuring optimal electricity costs under the worst-case scenario. The bargaining transfer problem further solves the cost allocation scheme for charging and discharging operations of shared energy storage among users

building upon the cost minimization results. This model not only accounts for the impact of load forecast uncertainty on maximum demand but also provides a theoretical framework for incentivizing coordinated cooperation among shared energy storage alliance members and exploring equitable transaction mechanisms. Finally

case simulations verify the feasibility and effectiveness of the proposed monthly maximum demand optimization model.