Research on Rolling Co-Optimization of Fault Repair and Service Restoration in Distribution Network Based on Combined Drive Methodology

In this paper, a fault repair and service restoration framework in response to extreme disasters is proposed from the perspective of combined data-driven and knowledge-driven. Firstly, theoretical analysis on the process of fault information acquisition is conducted and an emergency decision-making system based on big data platform is designed. Secondly, a two-stage mixed integer linear programming (MILP) based mechanism model is propounded to ensure the timeliness and quality of repair and restoration work. The first stage is to cluster repair tasks considering geographical distance and relevant constraints, which can reduce the complexity of the problem for large distribution network. The second stage is to solve the co-optimization problem of fault repair and service restoration, which is formulated as a bilevel model. The dispatching of repair crews is optimized in lower-level model considering real-time traffic conditions, traveling time and emergency resources constraints. Network reconfiguration plan for maximizing economic benefits is proposed in upper-level model. The service restoration plans for subsequent periods are revised dynamically according to the process of repair work. Finally, the PG&E69-bus system is taken as an example and the simulation results verify the effectiveness of the model