Location, Sizing, and Scheduling Optimization Model for Distributed Energy Storage under Demand Uncertainty

As renewable energy penetration increases, load forecasting errors challenge system operators to balance variable supply and demand, To address this challenge, this paper proposes a two-stage optimization model for distributed energy storage (DES)planning under demand uncertainty. The proposed model first optimizes DES site locations and capacities based on cost factors like infrastructure costs and emissions impacts, The second stage then minimizes electricity procurement costs through optimal daily operational scheduling for each DES station, This includes balancing energy transfers to manage peak loads and fulfilling unpredictable demand across the distribution grid, By formulating the problem using mixed. integer nonlinear programming and a heuristic algorithm, the model can strategically plan DES investments despite load forecast uncertainties, Simulation results using real energy storage data demonstrate the model's effectiveness in determining robust DES deployments, This leads to reductions in long-term operational costs while improving renewable energy integration and distribution grid flexibility.