Post-Earthquake Restoration of Electricity-Gas Distribution Systems with Damage Information Collection and Repair Vehicle Routing
For operators of critical infrastructure, this work addresses the realistic scenario of unknown damage after disasters, enabling more efficient restoration decisions.
This paper presents an adaptive restoration framework for integrated electricity-gas distribution systems (IEGDS) after earthquakes, using a POMDP formulation and a belief tree search algorithm. The method reduces total outage cost by over 15% compared to stochastic programming and heuristic strategies, achieving costs comparable to the ideal solution.
Extreme events such as earthquakes pose significant threats to integrated electricity-gas distribution systems (IEGDS) by causing widespread damage. Existing restoration approaches typically assume full awareness of damage, which may not be true if monitoring and communication infrastructures are impaired. In such circumstances, field inspection is necessary. This paper presents a novel adaptive restoration framework for IEGDS, considering dynamic damage assessment and repair. The restoration problem is formulated as a partially observable Markov decision process (POMDP), capturing the gradually revealed contingency and the evolving impact of field crew actions. To address the computational challenges of POMDPs in real-time applications, an advanced belief tree search (BTS) algorithm is introduced. This algorithm enables crew members to continuously update their actions based on evolving belief states, leveraging comprehensive simulations to evaluate potential future trajectories and identify optimal inspection and repair strategies. Based on the BTS algorithm, a unified real-time decision-making framework is developed for IEGDS restoration. Case studies on two distinct IEGDS systems demonstrate the effectiveness and scalability of the proposed method. The results indicate that the proposed approach achieves an outage cost comparable to the ideal solution, and reduces the total outage cost by more than 15% compared to strategies based on stochastic programming and heuristic methods.