Co-Optimization of Damage Assessment and Restoration: A Resilience-Driven Dynamic Crew Allocation for Power Distribution Systems

TL;DR

This paper presents a MILP model for dynamic crew allocation in power systems, optimizing damage assessment and restoration processes to improve resilience and operational efficiency during outages.

Contribution

It introduces a novel co-optimization framework that dynamically adapts crew dispatch decisions based on evolving damage and repair information.

Findings

Effective in large-scale test systems (IEEE 123-node and 8500-node)

Provides bounds for voltage levels and power flows under different scenarios

Enhances operational decision-making during power restoration

Abstract

This study introduces a mixed-integer linear programming (MILP) model, effectively co-optimizing patrolling, damage assessment, fault isolation, repair, and load re-energization processes. The model is designed to solve a vital operational conundrum: deciding between further network exploration to obtain more comprehensive data or addressing the repair of already identified faults. As information on the fault location and repair timelines becomes available, the model allows for dynamic adaptation of crew dispatch decisions. In addition, this study proposes a conservative power flow constraint set that considers two network loading scenarios within the final network configuration. This approach results in the determination of an upper and a lower bound for node voltage levels and an upper bound for power line flows. To underscore the practicality and scalability of the proposed model, we have demonstrated its application using IEEE 123-node and 8500-node test systems, where it delivered promising results.