Distribution Network Restoration with Mobile Resources Dispatch: A Simulation-Based Online Dynamic Programming Approach

TL;DR

This paper introduces a simulation-based online dynamic programming method for dispatching mobile resources to restore distribution systems rapidly after extreme events, effectively handling uncertainties and real-time information.

Contribution

It develops an efficient, simulation-based online decision-making approach that improves restoration performance without relying on offline training or historical data.

Findings

Achieves higher efficiency in distribution system restoration.

Outperforms rolling horizon optimization in case studies.

Adapts effectively to real-time information updates.

Abstract

Dispatching mobile resources such as repair crews and mobile emergency generators is essential for the rapid restoration of distribution systems after extreme events. However, the restoration process is affected by various uncertain factors including repair time, road condition, and newly observed failures, necessitating online decision-making in response to real-time information. This paper proposes a simulation-based online dynamic programming approach to provide real-time restoration actions considering the dispatch of mobile resources. Using an index-based priority rule as the base policy, the remaining cumulative loss at the current state and a given action is evaluated from online simulation. As the base policy is explicit, the simulation is efficient. Then, the action leading to the minimum cumulative loss is chosen. It is proven that such a strategy improves the performance of base policy. The proposed policy adapts to real-time information updates and does not rely on offline training, so incurs no data and convergence-related issues, which is important in restoration tasks where the historical data of extreme events is rare. The rolling optimization approach may not meet the requirement of online use, because routing mobile resources gives rise to large-scale discrete optimization problems. Case studies on 123-bus and 8500-bus systems demonstrate that the proposed method achieves higher efficiency and better performance compared with rolling horizon optimization.