Distributionally Robust Low‐Carbon Economic Dispatch of Microgrids Considering AGC‐Capable Regulation Resources and Industrial Demand Response

The increasing penetration of wind and photovoltaic (PV) generation introduces significant source‐load uncertainty into low‐carbon microgrid economic dispatch. Conventional deterministic scheduling cannot hedge against prediction errors, while traditional robust optimization based on fixed uncertainty sets may lead to overly conservative dispatch results. To address these issues, this paper proposes a distributionally robust optimization (DRO) method for low‐carbon economic dispatch considering automatic generation control (AGC)‐capable regulation resources and industrial demand response. First, AGC‐capable units, energy storage and multiple energy‐intensive industrial loads, including aluminium electrolysis, cement and steel loads, are modelled as coordinated flexibility resources. Then, historical wind/PV/load prediction‐error samples are used to construct a data‐driven ambiguity set to describe distributional uncertainty without assuming an exact probability distribution. Based on the original dispatch framework, a DRO model is formulated to coordinate AGC regulation, storage operation and industrial load adjustment under the worst‐case distribution. A column‐and‐constraint generation (C&CG) algorithm is adopted to solve the reformulated model. Case studies on a modified IEEE 30‐bus system show that the proposed method achieves a balanced trade‐off between economy and robustness. Compared with deterministic optimization, the out‐of‐sample expected cost is reduced by 2.83%; compared with traditional robust optimization, the day‐ahead dispatch cost is reduced by 5.55%.