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 rapid digitalization of the power sector, coupled with increasing penetration of renewable energy sources and distributed energy resources, has fundamentally transformed the structure and operation of modern power systems. Traditional model-based and ruledriven approaches are increasingly challenged by the growing complexity, uncertainty, and scalability requirements of smart grids. In this context, artificial intelligence (AI) has emerged as a key enabling technology for enhancing forecasting accuracy, operational efficiency, system resilience, and cybersecurity. This paper presents a comprehensive review of AI applications in smart grids and modern power systems, covering methodological advances and practical implementations across multiple domains. A structured taxonomy of AI techniques-including machine learning, deep learning, reinforcement learning, and hybrid and explainable AI-is provided, along with a critical discussion of their suitability for power system applications. Key application areas such as load and renewable energy forecasting, power system operation and control, fault detection and predictive maintenance, demand response, distributed energy resource management, and grid resilience and security are systematically reviewed. Furthermore, the paper examines performance evaluation practices, benchmarking challenges, and deployment barriers related to data availability, scalability, interpretability, and regulatory constraints. Emerging research directions, including AI-driven digital twins, federated learning, foundation models, and AI-enabled net-zero grid operation, are also discussed. By synthesizing recent advances and identifying open research challenges, this review aims to provide valuable insights for researchers and practitioners seeking to develop reliable, scalable, and trustworthy AI solutions for next-generation power systems.
Carbon dioxide capture and utilization (CCU) and Power-to-X (PtX) technologies are increasingly viewed as key enablers of net-zero energy systems, particularly for decarbonizing sectors where direct electrification remains challenging. While a rapidly expanding literature has examined individual CCU and PtX processes, existing reviews largely adopt technology-centric perspectives with inconsistent system boundaries and assumptions, limiting comparability and obscuring system-level climate relevance. This review addresses this gap by providing an integrated, system-level synthesis of CO2 capture, utilization, and Power-to-X pathways for sustainable fuels and energy systems. Building on a harmonized analytical framework, the review evaluates major capture routes, utilization pathways, and synthetic fuel options under consistent assumptions, with particular emphasis on energy demand, net CO2 reduction, and sensitivity to electricity carbon intensity. The analysis demonstrates that overall performance is governed less by component-level efficiencies than by a small set of dominant system drivers, notably electricity carbon intensity, hydrogen demand, and CO2 source characteristics. Results reveal substantial trade-offs between energy input, carbon mitigation potential, and deployment robustness, highlighting that CCU–PtX pathways are inherently context-dependent rather than universally beneficial. By consolidating fragmented insights into a unified decision-oriented framework, this review clarifies where and under what conditions CCU–PtX technologies can deliver meaningful climate benefits. The findings provide actionable guidance for researchers, industry, and policymakers seeking to deploy CCU–PtX pathways strategically within net-zero energy transitions.
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.
