[Paper Review] Community-Centered Resilience Enhancement of Urban Power and Gas Networks via Microgrid Partitioning, Mobile Energy Storage, and Data-Driven Risk Assessment

Arya Abdollahi|arXiv (Cornell University)|Feb 10, 2026

Integrated Energy Systems Optimization0 citations

TL;DR

The paper proposes a community-centered framework to enhance urban power and gas network resilience by integrating microgrid partitioning, mobile energy storage, and data-driven risk assessment, enabling self-healing networks and robust decision-making under disruptions.

ABSTRACT

Urban energy systems face increasing challenges due to high penetration of renewable energy sources, extreme weather events, and other high-impact, low-probability disruptions. This project proposes a community-centered, open-access framework to enhance the resilience and reliability of urban power and gas networks by integrating microgrid partitioning, mobile energy storage deployment, and data-driven risk assessment. The approach involves converting passive distribution networks into active, self-healing microgrids using distributed energy resources and remotely controlled switches to enable flexible reconfiguration during normal and emergency operations. To address uncertainties from intermittent renewable generation and variable load, an adjustable interval optimization method combined with a column and constraint generation algorithm is developed, providing robust planning solutions without requiring probabilistic information. Additionally, a real-time online risk assessment tool is proposed, leveraging 25 multi-dimensional indices including load, grid status, resilient resources, emergency response, and meteorological factors to support operational decision-making during extreme events. The framework also optimizes the long-term sizing and allocation of mobile energy storage units while incorporating urban traffic data for effective routing during emergencies. Finally, a novel time-dependent resilience and reliability index is introduced to quantify system performance under diverse operating conditions. The proposed methodology aims to enable resilient, efficient, and adaptable urban energy networks capable of withstanding high-impact disruptions while maximizing operational and economic benefits.

Motivation & Objective

• Motivate resilience of urban energy systems against high-impact, low-probability disruptions and increasing renewable penetration.
• Convert passive distribution networks into active, self-healing microgrids using distributed energy resources and remotely controlled switches.
• Provide robust planning and real-time risk assessment without requiring probabilistic data.
• Optimize long-term sizing and routing of mobile energy storage with urban traffic considerations.
• Introduce a time-dependent resilience and reliability index to quantify performance under diverse conditions.

Proposed method

• Develop an adjustable interval optimization method paired with a column and constraint generation algorithm to handle uncertainties without probabilistic information.
• Create a real-time online risk assessment tool using 25 multi-dimensional indices (load, grid status, resilient resources, emergency response, meteorology, etc.).
• Integrate mobile energy storage sizing and allocation with traffic data to enable effective emergency routing.
• Partition distribution networks into microgrids and enable remote switching to support reconfiguration in normal and emergency operations.
• Propose a time-dependent resilience and reliability index to quantify system performance across scenarios.

Experimental results

Research questions

• RQ1 How can urban power and gas networks be converted into self-healing microgrids to improve resilience during extreme events?
• RQ2 How can mobile energy storage be optimally sized, allocated, and routed considering urban traffic to support emergency response?
• RQ3 Can data-driven risk assessment with multi-dimensional indices provide real-time operational support under high-impact disruptions without probabilistic input?
• RQ4 What metrics best capture time-dependent resilience and reliability of urban energy systems under diverse operating conditions?
• RQ5 How does the proposed framework perform under uncertainties from intermittent renewable generation and varying loads?

Key findings

• A framework that combines microgrid partitioning, mobile energy storage, and data-driven risk assessment to enhance resilience and reliability of urban energy networks.
• An adjustable interval optimization with a column and constraint generation algorithm enables robust planning without probabilistic information.
• A real-time online risk assessment tool leveraging 25 indices supports operational decision-making during extreme events.
• The approach accounts for urban traffic data in routing mobile storage for emergencies.
• Introduction of a time-dependent resilience and reliability index to quantify performance across diverse operating conditions.