Distributionally robust scheduling of multi-energy virtual microgrids under wind power uncertainty: A generalized Chebyshev approach

The increasing integration of renewable energy into conventional distribution networks poses significant operational challenges due to its inherent uncertainty and volatility. To address these issues, the concept of virtual microgrids (VMGs) has emerged as a promising paradigm. However, existing studies often either neglect the advantages of multi-energy integration or adopt simplistic treatments of uncertainty, limiting their practical applicability in energy management. This paper proposes a novel distributionally robust chance-constrained energy and reserve scheduling (DRCC-E&RS) framework tailored for VMGs. The model incorporates wind power uncertainty using a generalized Chebyshev ambiguity set, ensuring robustness without assuming specific probability distributions. To enhance computational tractability, the original formulation is reformulated into a second-order cone programming (SOCP) problem, thereby avoiding the scalability limitations associated with semi-definite programming (SDP) solvers. Extensive case studies on the IEEE 33-, 69-, and 136-bus systems validate the effectiveness of the proposed method, demonstrating superior out-of-sample performance and computational efficiency compared to Gaussian-based, scenario-based, typical Chebyshev-based and traditional SDP-based approaches.