Distributionally robust optimal scheduling method for transmission and distribution coordination considering power curtailment strategy

With the continuous increase in renewable energy penetration and the widespread integration of distributed resources in the power grid, the interaction between transmission and distribution networks has become increasingly complex, and traditional dispatching approaches have been found inadequate in balancing the overall overall economic efficiency and system security. To address the challenge of coordinated transmission and distribution networks scheduling under high renewable energy penetration, a distributionally robust optimal scheduling method for transmission and distribution coordination that considers the power curtailment strategy is proposed in this paper. In the proposed method, a local affine control strategy, coupled with curtailment mechanism, is introduced to form a collaborative response mechanism with adjustable resources. At the modeling level, uncertainty constraints are reconstructed using a distributionally robust chance-constrained approach based on Wasserstein distance, and are transformed into a set of linear constraints through conditional value-at-risk approximation, enabling their incorporation into a convex optimization framework. A distributed optimization algorithm based on heterogeneous decomposition is employed, through which global resource coordination is achieved via the alternating iteration of boundary prices and exchanged power. Simulation studies are conducted on the modified T39-D33 and T118-D69 test systems. The results demonstrate that the proposed method is effective in handling renewable generation fluctuations, fully utilizing the bi-directional reserve support capabilities of coordinated transmission and distribution networks, and promoting secure and economically efficient operation under integrated scheduling.