Co-optimization of production networks and shared energy storage systems in industrial energy system under uncertainty

As renewable energy penetration rises, industrial production faces severe energy management challenges. To address the uncertainties of wind and solar power generation alongside industrial production's multifaceted demands for efficiency, economic, and environmental performance, this paper proposes a two-stage co-optimization model for industrial production networks and shared energy storage systems based on interactive multi-objective decision making. In the first stage, the storage capacity and power rating of the shared energy storage system, as well as the production schedule based on the state-task network (STN), are determined. The second stage performs dispatch optimization under wind and solar generation uncertainty. To address multi-objective conflicts and capture decision-maker preferences, the analytic hierarchy process (AHP) is used to generate a Pareto-approximate solution set. User satisfaction enables a backtracking mechanism for adjusting preferences dynamically, and the technique for order preference by similarity to ideal solution (TOPSIS) selects the optimal compromise solution. Case studies demonstrate that the proposed stochastic optimization model translates to a 7.1% reduction in total operational costs and a 16.4% enhancement in energy storage utilization compared to the deterministic standalone-storage case. Furthermore, the model mitigates the load fluctuation penalty by 2.4%, significantly balancing multi-objective trade-offs and providing more robust decision support for green industrial-park operations.