Commonwealth Edison, commonly known by syllabic abbreviation as ComEd, is the largest electric utility in Illinois, and the sole electric provider[citation needed] in Chicago and much of Northern Illinois. Its service territory stretches roughly from Iroquois County on the south to the Wisconsin border on the north and from the Iowa border on the west to the Indiana border on the east. For more than 100 years, Commonwealth Edison has been the primary electric delivery services company for Northern Illinois. Today, ComEd is a unit of Chicago-based Exelon Corporation, one of the nation's largest electric and gas utility holding companies. ComEd provides electric service to more than 3.8 million customers across Northern Illinois. The company's revenues total more than $15 billion annually.[citation needed]As of 2015[update], ComEd has interconnections with We Energies, ITC Midwest, Ameren, American Electric Power, Northern Indiana Public Service, and MidAmerican Electric (MEC).
The proliferation of distributed energy resources (DERs) and the increasing frequency of high-impact events have introduced challenges for the reliability, resilience, and operational flexibility of modern electric power systems. Microgrids have emerged as a promising solution, enabling localized control, seamless transition between grid-connected and islanded modes, and enhanced integration of DERs. This article presents the design, implementation, and operational validation of the Bronzeville Community Microgrid (BCM), a pioneering utility-scale microgrid commissioned by Commonwealth Edison (ComEd) in Chicago, Illinois. The BCM integrates a diverse mix of customer loads and DERs, advanced automation, and a microgrid master controller (MMC) to manage dynamic operating modes and adaptive protection. The article details the technical architecture, control strategies, and functionality validation through hardware-in-the-loop simulations and field tests, demonstrating the BCM’s capabilities for planned islanding, grid reconnection, and clustering with the Illinois Institute of Technology campus microgrid. Lessons learned and strategic opportunities for future microgrid deployments are discussed, highlighting the role of microgrids in advancing resilient, distributed, and intelligent power systems.
Reliable photovoltaic defect identification is essential for maintaining energy yield, ensuring warranty compliance, and enabling scalable inspection of rapidly expanding solar fleets. Although recent advances in computer vision have improved automated defect detection, most existing systems operate as opaque classifiers that provide limited diagnostic insight for high-stakes energy infrastructure. Here we introduce REVL-PV, a vision-language framework that embeds domain-specific diagnostic reasoning into multimodal learning across electroluminescence, thermal, and visible-light imagery. By requiring the model to link visual evidence to plausible defect mechanisms before classification, the framework produces structured diagnostic reports aligned with professional photovoltaic inspection practice. Evaluated on 1,927 real-world modules spanning eight defect categories, REVL-PV achieves 93% classification accuracy while producing interpretable diagnostic rationales and maintaining strong robustness under realistic image corruptions. A blind concordance study with a certified solar inspection expert shows strong semantic alignment between model explanations and expert assessments across defect identification, root-cause attribution, and visual descriptions. These results demonstrate that reasoning-aware multimodal learning establishes a general paradigm for trustworthy AI-assisted inspection of photovoltaic energy infrastructure.
The electric distribution system is a cornerstone of modern life, playing a critical role in the daily activities and well-being of individuals. As the world transitions toward a decarbonized future, where even mobility relies on electricity, ensuring the resilience of the grid becomes paramount. This paper introduces novel resilience metrics designed to equip utilities and stakeholders with actionable tools to assess performance during storm events. The metrics focus on emergency storm response and the resources required to improve customer service. The practical calculation of the metrics from historical utility data is demonstrated for multiple storm events. Additionally, the metrics' improvement with added crews is estimated by "rerunning history" with faster restoration. By applying this resilience framework, utilities can enhance their restoration strategies and unlock potential cost savings, benefiting both providers and customers in an era of heightened energy dependency.
There has been considerable increase in the occurrence of adverse weather events like storms in recent years. The damage done due to these events is highly unpredictable and there is a lack of resilience frameworks to study their effects on distribution feeders (DFs). Having a good resilience framework helps in planning future infrastructure investments to fortify the DF against future adverse events. In this paper, we propose a generalized set of resilience metrics to evaluate the resilience of the DF both at the nodal and feeder levels. These resilience metrics are useful in analyzing realistic performance curves (PCs) of the DFs with multiple periods of performance degradation and recovery. Further, a greedy resilience enhancement planning strategy is proposed to find the most suitable locations to place new distributed generators (DGs) and improve DF resilience. Finally, the efficacy of the resilience framework and the resilience enhancement planning strategy is evaluated using historical storm data available for a real-world feeder (Feeder 91).
There has been a growing incidence of adverse weather events leading to substantial power black outs in recent years. Proper hardening of the distribution system significantly improves its resilience to extreme climatic conditions. In this paper, we propose a set of four resilience indices to evaluate the resilience of the distribution system from various perspectives and combine them into a single index to get a holistic measure of distribution feeder resilience. This resilience framework has the capability to analyze realistic performance curves (PCs) of the distribution system with multiple periods of performance degradation and recovery. Additionally, a greedy resilience hardening strategy is proposed which uses the resilience framework and historical storm outage data for determining the set of lines to be hardened to maximally improve the resilience of the distribution feeder. The proposed resilience framework and greedy line hardening strategy are implemented on a real-world distribution feeder (Feeder 91) to demonstrate their efficacy.
