Distributed Distributionally Robust Scheduling for Coordinated Transmission and Distribution Systems Considering Decision-Dependent Uncertainties

As the proportion of centralized and distributed new energy sources in the power system increases, the proportion of controllable generation units decreases and the uncertainty grows significantly. Independent scheduling of the transmission and distribution system (TDS) makes it difficult to address the power balance challenges. To improve the power system's operational flexibility and power supply reliability, this paper proposes a two-stage distributionally robust optimization (DRO) cooperative scheduling model for TDS. During the collaborative process, the scheduling decision of the TDS will affect the range of boundary coupling variables, thus generating a decision-dependent uncertainty (DDU) problem. A novel decision-dependent probability distribution set is proposed by analyzing the influence of decision and uncertainty variable probability distributions. It provides a generalized framework for simultaneously addressing DDUs in boundary variables and decision-independent uncertainties (DIUs) in traditional source-load power. To address the computational challenges of DRO models under complex uncertainties and leverage the natural decoupling between TDS, a fully distributed approach that combines an improved Alternating Direction Method of Multipliers (ADMM) with an adaptive Column and Constraint Generation (A-C&CG) is proposed. Two case studies show the advantages of the proposed model and approach, verifying their effectiveness and the necessity of considering DDUs.