Medium- and Long-Term Optimal Stochastic Scheduling for Inter-Basin Hydro-Wind-Photovoltaic Complementary Systems Considering Wind and Solar Output Uncertainty

With the large-scale integration of wind power and photovoltaic (PV) into the grid, dealing with their output uncertainties and formulating more reliable scheduling strategies has become a critical challenge for the efficient operation of hydropower-dominated inter-basin hydro-wind-PV complementary systems. To quantify the uncertainty associated with wind and PV power generation, this paper proposes a method for generating wind and PV power output scenarios, combining adaptive diffusion kernel density estimation with Copula theory. Scenario reduction is then carried out using the K-means clustering algorithm. Based on this, a medium- and long-term stochastic expectation model for the inter-basin hydro-wind-PV complementary system is developed. The model is subsequently solved using the Gurobi 11.0.3 optimization solver within the MATLAB environment. A case study is conducted based on a selected inter-basin hydro-wind-PV clean energy base in China. The results demonstrate that the proposed scheduling strategy effectively addresses the unpredictability of wind and solar power, improves the overall utilization of renewable energy sources, and facilitates more efficient water level regulation at each power station. Furthermore, it significantly enhances the overall performance and efficiency of the complementary system.