A Wasserstein Distance-Based Distributionally Robust Optimization Strategy for a Renewable Energy Power Grid Considering Meteorological Uncertainty

With the large-scale integration of renewable energy into the power system, meteorological uncertainty poses challenges to the safe and stable operation of the system. Traditional uncertainty optimization methods struggle to balance robustness and economy. This paper proposes a Wasserstein distance-based distributionally robust optimization strategy that considers covariate factors for a renewable energy power grid considering meteorological uncertainty. By introducing covariate factors to construct the Wasserstein ambiguity set, the intrinsic connection between weather uncertainty and the output of new energy is effectively depicted. The optimization problem is transformed into a solvable form of mixed integer linear programming by using linear decision rules and duality theorems, and the distributionally robust optimization scheduling problem is solved based on the improved cross optimization algorithm. Simulation results based on the IEEE 33 system show that under the same worst-case expected energy shortage of 20 kWh, the proposed method achieves an expected total dispatch cost of approximately CNY 0.534 million, reducing cost by about 0.4%, 0.9%, and 1.8% compared with conventional Wasserstein DRO, KL-divergence DRO, and Moment Information DRO; when the radius of the Wasserstein ball is 1, using the CSO algorithm reduces the runtime by 59.4% compared with the solver. It effectively reduces operating costs and solution speed while ensuring system security, offering a new approach for the optimal dispatch of power systems with a high penetration of renewable energy.