Distributionally Robust Joint Chance-Constrained Scheduling for Integrated Energy System Considering Symmetric-Unimodality Information of Uncertainty

With the increasing penetration of renewable energy sources, their unpredictable characteristics bring remarkable challenges to the stable operation of integrated heat and electricity systems. In this paper, we propose a distributionally robust (DR) joint chance-constrained scheduling model considering renewable energy uncertainties. Firstly, an optimal scheduling model of integrated energy system including conventional generators, wind power plants, combined heat and power units and electric boilers is developed taking into account system operational constraints such as heat transfer time-delay comprehensively. A moment-based ambiguity is established to characterize the uncertainty whose symmetric and unimodality information is taken into consideration. The adopted ambiguity notably reduces conservatism compared to single-moment metrics. By introducing optimized Bonferroni approximation (OBA), the DR joint chance constraints are transformed into DR individual chance constraints. Due to the intractability of OBA formulation, we employ convex approximations to convert it into tractable forms. Case studies performed on the test system demonstrate that the proposed methodology can effectively reduce system costs and conservatism.