Flexible resource aggregation two-stage scheduling optimization model considering multiple uncertainties in the market environment

Abstract

High shares of wind and photovoltaic (PV) generation increase the flexibility needed to balance uncertain net load. This study develops a two-stage robust capacity-planning model for controllable distributed generation, battery storage, and hydrogen energy storage under wind, PV, and load uncertainty. Upward and downward flexibility requirements are first derived from feasible net-load intervals. These requirements are then embedded in a min-max-min planning model: capacities are selected before uncertainty is observed, the uncertainty set identifies an adverse realization, and operating decisions are optimized afterward. The model is solved by a column-and-constraint generation (C&CG) algorithm with a KKT-based subproblem reformulation. The case study uses 8,760 synchronized hourly forecast and realized observations from a provincial grid in Northwest China, and the uncertainty radii are calibrated from empirical 95th percentiles. At Γ = 12, the optimal capacities are 2,472.30 MW for distributed generation, 852.40 MW for battery storage, and 446.20 MW for hydrogen energy storage. Compared with deterministic planning, the robust solution increases distributed-generation capacity by 13.8% and battery-storage capacity by 11.2%, while comprehensive cost increases by approximately 16.4%. Relative to conventional full-box robustness, the proposed solution reduces comprehensive cost by approximately 3.7%. The dispatch results show that batteries primarily cover short-duration imbalances, while hydrogen storage provides longer-duration support.