Distributionally robust optimal scheduling of integrated energy system considering adaptive Copula function and dynamic reserve

Deploying an integrated energy system represents a critical pathway to alleviate energy supply pressure and improve energy efficiency. However, in existing works on the integrated energy system, the uncertainties and multi-type reserves over different scheduling stages have not been fully considered to warrant the stable operation performance of integrated energy systems under multiple scenarios. Based on these considerations, a distributed robust optimal scheduling of an integrated energy system considering adaptive Copula function and dynamic reserve is proposed. First, an adaptive Copula function is developed to accurately describe the dynamic correlation of wind/solar power output and the characteristics of joint output. At the same time, the quasi-Monte Carlo method is used to form a typical scenario set aiming at the uncertainty of power generation for renewable energy sources. Furthermore, the reserve provision model is proposed, and the ineffective upward reserve, ineffective downward reserve, loss load, and power curtailment are respectively developed to address the effect caused by the uncertainty of renewable energy sources. Then, based on scenario information of renewable energy sources, the operating cost in the day-ahead stage and the adjustment cost of the system under the worst scenario in the real-time stage are taken as the optimization objectives, and a two-stage distribution robust optimization scheduling model is constructed. The two-stage model is solved using a column-and-constraint generation algorithm. Finally, case studies are carried out to verify by Gurobi that the operation cost of distributionally robust optimization is $2.0704\times 10^4\$$, the lowest ineffective reserve cost of 208$ is the lowest, the proposed method has a good economy and robustness and is suitable for dealing with the uncertainty of renewable energy sources.