Distributionally Robust Optimization Model for Energy Bases Based on Wasserstein Distance

Due to the large-scale integration of new energy sources, the stability of the power grid has been severely challenged. Based on the characteristics of Fujian Province, this paper proposes an energy base model that integrates pumped storage, hydrogen storage, battery storage, wind power, thermal power, and nuclear power. By analyzing their complementary characteristics, the model addresses the integration of large-scale offshore wind power. Considering the uncertainty of wind power, a distributionally robust optimization model for the energy base is proposed based on the Wasserstein distance, and its superior robustness is demonstrated through case studies. The results show that the distributionally robust optimization approach can effectively enhance the robustness of the model while avoiding overly conservative outcomes.