Chance-constrained optimization of multi-energy complementary power generation system considering multiple uncertainties

To mitigate the impact of integrating renewable energy resources into the power grid, this paper proposes an innovative method using Cholesky decomposition and fuzzy chance-constrained programming. This method is used to develop an linear optimal scheduling model for a multi-energy complementary power generation system that considers uncertainties in wind, photovoltaic, and load. Firstly, uncertainties of wind output, photovoltaic output, and load output are processed based on the fuzzy chance constraint method. Then, an optimal scheduling model is formulated for a multi-energy complementary power generation system, aiming to minimize the system's operating costs. Finally, the proposed model and method are validated through example simulations, which provides a reliable scheduling scheme for the combined complementary power generation system.