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

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.

💡 Research Summary

This paper presents an integrated, community‑centered framework to boost the resilience and reliability of urban power and gas distribution networks that are increasingly penetrated by renewable energy sources and exposed to high‑impact, low‑probability (HILP) events such as extreme weather, cyber‑attacks, and infrastructure failures. The authors propose a three‑pronged approach: (1) partitioning the existing passive distribution system into a set of actively managed microgrids (μGs) that can operate both in an interconnected mode and in a self‑healing island mode; (2) deploying mobile energy storage systems (MESS) whose long‑term sizing, placement, and short‑term routing are jointly optimized using urban traffic data; and (3) a data‑driven, real‑time risk assessment tool that aggregates 25 multidimensional indices—including load, grid status, resilient resources, emergency response capacity, and meteorological factors—to guide operational decisions during emergencies.

To handle the severe uncertainties inherent in renewable generation and load variability without relying on explicit probability distributions, the authors develop an Adjustable Interval Optimization (AIO) model. Uncertain parameters are represented as bounded intervals, and a Column‑and‑Constraint Generation (C&CG) algorithm iteratively refines the master problem (design variables such as μG boundaries, DER capacities, and MESS sizes) and the sub‑problem (worst‑case interval realizations). This yields a robust planning solution that converges quickly and scales to city‑wide networks.

The microgrid partitioning methodology incorporates remotely controlled switches (RCS) that enable flexible electrical and geographical reconfiguration. In normal operation, the objective maximizes profit (revenue minus operating costs) by optimally dispatching distributed energy resources (DER) and allowing power exchange among μGs. In island mode, the focus shifts to minimizing load shedding and maximizing reliability through re‑dispatch, load shedding, and intra‑μG power sharing. The authors formulate the problem as a linear, convex model to guarantee global optimality and computational tractability.

A novel aspect is the integration of mobile energy storage. The long‑term sizing and siting problem determines the capacity and location of each MESS unit, accounting for investment, operation, and degradation costs. For short‑term deployment, the framework uses real‑time traffic flow, road network topology, and GIS data to compute optimal routes that minimize travel time and ensure that storage units arrive where they are most needed during an outage. This dual‑time‑scale optimization aligns strategic planning with tactical emergency response.

The real‑time risk assessment engine continuously monitors the 25 indices, computes a composite risk score, and visualizes the result on an operator dashboard. When the score exceeds predefined thresholds, the system automatically suggests microgrid reconfiguration actions, DER adjustments, or MESS dispatches. The authors also introduce a time‑dependent resilience‑reliability index that extends traditional reliability metrics (e.g., SAIDI, SAIFI) by incorporating island‑mode performance, DER and MESS availability, and the severity of external threats. This index enables quantitative comparison of system performance across normal, disrupted, and recovery phases.

A case study is carried out on the Detroit distribution network, using five years of load profiles, hourly market prices, DER cost data, and Michigan traffic information. Simulations show that the proposed microgrid‑RCS configuration reduces annual operating costs by about 7 % and cuts load shedding in island mode by 45 %. The risk‑score‑driven reconfiguration shortens average restoration time by roughly three hours, while the traffic‑aware MESS routing reduces travel time by 22 % compared with naïve dispatch. Overall, the new resilience‑reliability index improves by 18 % relative to a baseline that ignores island‑mode and emergency resources.

In conclusion, the paper delivers a comprehensive, data‑rich, and computationally efficient methodology that bridges planning, operation, and emergency management for urban energy systems. It fills several research gaps: robust optimization without probabilistic inputs, explicit modeling of self‑healing island operation, integration of traffic dynamics into mobile storage logistics, and a unified resilience‑reliability performance metric. Future work is suggested on extending the framework to multi‑hazard cyber‑physical scenarios, tighter coupling of gas and electricity networks, and embedding AI‑based predictive analytics to further enhance the real‑time risk assessment capability.