Distribution Robust Optimization of Electricity Grid Load Control

To address the issues of distribution network overload and insufficient flexibility caused by the increasing share of renewable energy, a data-driven distributionally robust optimization scheduling method is proposed. This method considers the spatiotemporal correlation of wind and solar output, utilizing Copula theory to construct the power output set, quantifying the flexibility demand of the power system and establishing supply-demand balance constraints. Additionally, the two-stage distributionally robust model is formulated with the objective of optimizing the operational cost of flexibility resources and the penalty cost for flexibility shortfall. The model is then decoupled into a master problem and sub-problem, solved using a column-and-constraint generation algorithm. Simulation results demonstrate that the proposed model effectively improves the flexibility margin and economic efficiency of the power system.