Distributionally Robust Joint Chance-Constrained Dispatch for Integrated Transmission-Distribution Systems via Distributed Optimization

This paper focuses on the distributionally robust dispatch for integrated transmission-distribution (ITD) systems via distributed optimization. Existing algorithms usually require synchronization of all subproblems, which could be hard to scale, resulting in under-utilization computation resources due subsystem heterogeneity ITD systems. Moreover, most commonly used individual chance-constrained models cannot systematically and robustly ensure simultaneous security constraint satisfaction. To address these limitations, this presents a novel joint (DRJCC) model asynchronous decentralized Using Wasserstein-metric based ambiguity set, we propose data-driven DRJCC transmission distribution systems, respectively. Furthermore, combined Bonferroni conditional value-at-risk approximation chance constraints is adopted transform into tractable conic formulation. Meanwhile, considering different grid scales complexity subsystems, tailored alternating direction method multipliers (ADMM) algorithm that better adapts star topological proposed. scheme only requires local communications allows each operator perform updates with information from subset of, but not all, neighbors. Numerical results illustrate effectiveness scalability proposed model.