基于分布鲁棒优化的电-气-热综合能源系统日前经济调度

The traditional robust optimization and stochastic optimization methods result in some limitations and shortcomings in dealing with the uncertainty problem of the renewable energy generation, such as wind power generation. Based on distributionally robust optimization, this paper focuses on the day-ahead economical dispatch problem of electricity-gas-heat integrated energy system (EGH-IES) considering the uncertainty of wind power. The Kullback-Leibler (KL) divergence is taken as a measure of the distance between a distribution function and the nominal distribution, and an ambiguity set of distribution function of wind power is constructed. Then, taking the total operating cost of the system as the objective function, a robust chance constrained optimization model for day-ahead economic dispatch of EGH-IES is established. Moreover, this model is transformed into a deterministic mixed integer linear optimization model which can be solved by commercial solvers. Finally, case studies demonstrate the effectiveness of the proposed method, and the effects of technology of power converted into gas and transmission delay of the heating network on wind power consumption are discussed.