Coordinating O&M and Logistical Resources to Enhance Post-Disaster Resilience of Interdependent Power and Natural Gas Distribution Systems

TL;DR

This paper presents a comprehensive, optimized strategy for enhancing the resilience of interdependent power and natural gas systems post-disaster by coordinating emergency operations, logistics, demand response, and repair processes.

Contribution

It introduces a mixed-integer second-order cone programming model that integrates resource coordination, demand response, and repair scheduling for resilient system recovery.

Findings

The proposed model effectively improves system resilience in case studies.

Coordinated resource management reduces recovery time.

Integrated demand response alleviates operational stresses.

Abstract

Electric power and natural gas systems are becoming increasingly interdependent, driven by the growth of natural gas-fired generation and the electrification of the gas industry. Recent energy crises have underscored the urgent need for enhanced resilience in these interdependent systems. In response to this challenge, this paper focuses on the interdependent electric power and natural gas distribution systems and proposes a comprehensive strategy for enhancing their resilience, which involves optimally coordinating various resources encompassing emergency operation and maintenance (O&M) and logistical support, to efficiently restore damaged services and infrastructure. Specifically, generators, including those with fuel switching capability, are dispatched to provide emergency power, with the logistical process of fuel supply being particularly managed by coordinated scheduling of mobile fuel tankers. Additionally, integrated demand responses for power and gas are dispatched to alleviate operational stresses using a zonewise approach that groups customers with intertwined energy supplies into individual, dispatchable units. Furthermore, the strategy incorporates the repair of damaged facilities, with particular consideration given to the varying repair efficiencies dependent on the dispatched repair units, aiming to closely simulate actual repair scenarios. The strategy is encapsulated in a mixed-integer second-order cone programming model, with case studies conducted to validate its effectiveness.