Distributionally robust optimization for networked microgrids considering contingencies and renewable uncertainty

In light of a reliable and resilient power system under extreme weather conditions and natural disasters, microgrids integrating local renewable resource in a network have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to be addressed by conventional, deterministic microgrid energy management framework. The paper proposes a distributionally robust chance-constrained (DR-CC) framework for the power dispatch of networked microgrids. The model considers the power flow, dual-mode operation (i.e. on-/off-grid operation) and voltage droop control simultaneously. By analyzing the empirical solar generation forecast and uncertain forecast error distribution, we construct two kinds of ambiguity sets for the DR-CC model, so that the solution is robust for any unimodal forecast error distribution with specific shape and shared moments. We find a special ambiguity set, unimodal & symmetric set, and prove its tractable second-order conic formulation. It meets the reliability requirements verified by the robustness tests and shows less conservativeness in solution, compared with the unimodal ambiguity set in literature.