Distributionally Robust Joint Chance Constraint Planning for Multi-type Resilience Resources Considering Full Process Resilience Enhancement in Distribution Network

To address limitations in current distribution network resilience enhancement strategies, such as focusing on a single phase, using limited resilience resources, and insufficiently capturing the uncertainties of line failures, a distributionally robust joint chance constraint planning for multi-type resilient resources considering full process resilience enhancement in the distribution network is proposed in this paper. First, a fuzzy uncertainty set is established using the Wasserstein distance to model the strong coupling between wind field intensity, line failure probability, and wind turbine output thresholds. Subsequently, a bi-level three-stage planning model based on the "prevention-response-restoration" framework is developed for the joint deployment and scheduling of various resilience resources, capturing the unique temporal characteristics of extreme disasters. The model is then linearized and converted into a mixed-integer second-order cone programming problem by leveraging Conditional Value-at-Risk and strong duality theory. Finally, numerical simulations verify that the proposed approach enhances resilience and supply reliability of the distribution network under conditions of high uncertainty.