This paper analyzes the relationship between extreme wind conditions and power outages across all 254 Texas counties from 2014 to 2022. Hourly ERA5 reanalysis fields are aggregated to county boundaries and matched with distribution-level outage events from the EAGLE-I dataset. County-specific percentile thresholds identify severe wind, gust, and outage conditions, allowing comparison across regions with differing climatological and infrastructure characteristics. Two dominant outage mechanisms emerge: (i) exposure-driven wind sensitivity in the Texas Panhandle, and (ii) vegetation-driven gust sensitivity in East Texas. The resulting multi-year dataset supports systematic assessment of regional wind-outage sensitivity and provides empirical insights for risk analysis and resilience planning.

This paper introduces CaseBuilder, a modular, objectoriented workflow used to convert the EIA-860 database into a copper plate model of the generation fleet of the United States. Casebuilder improves on previous, more manual methods of creating cases from datasets. CaseBuilder includes a suite of data cleaning functions and multiple methods of interacting with the chosen power flow modeling software. It serves as the foundation for future, automated, case generation and modification workflows. In this example, using a publicly available generation inventory and power-flow modeling software, we recreated a case that researchers and engineers use to study the output of the US generation mix across both weather and time. We can now automate the production of updated versions of these cases, and to generate past versions of this case from previous versions of the dataset to examine what, if anything, has changed across the available timeframe. We can also rapidly iterate on this case, to explore potential changes to the generation fleet.

This letter uses sparse matrix statistics to highlight a structural gap between North American grid models and the synthetic cases commonly used in research. North American grids exhibit area-based modularity, characterized by dense intra-area links and limited inter-area connectivity. By contrast, synthetic grids often over-mesh across areas. Analysis shows that these surplus tie lines increase fills in the Jacobian factorization, which reduces realism and computational efficiency. The observations confirm that inter-area connectivity influences sparse matrix behavior. This work emphasizes the importance of incorporating modular structure into synthetic grid design.

This paper introduces an iterative, hybrid framework for solving the AC Optimal Power Flow (OPF) problem by integrating PowerWorld Simulator and PowerModels within a unified Python-driven workflow. The approach establishes a closed-loop optimization pipeline that uses PowerWorld for system modeling, exports solved power flow states to MATPOWER format, solves a nonlinear AC-OPF using PowerModels in Julia, and updates generator setpoints back into PowerWorld for the next iteration. Results demonstrate strong consistency between the simulation and optimization environments, validating the bidirectional data exchange and offering a practical template for hybrid OPF studies in complex grid environments.

This paper presents a process for quickly detecting and correcting alternative power flow solutions to the desired (operable) solution. As is well known, the positive sequence power flow has a potentially large number of solutions, with typically only one operable solution. Usually, the power flow algorithm correctly converges to this solution, but sometimes it converges to an undesired alternative solution that might be difficult to differentiate from the operable solution. To address this issue, the paper presents a method for quickly and reliably detecting these situations and for attempting to correct the undesired solution to the operable solution. Results are demonstrated on power systems ranging in size from three to 23,600 buses. The results show that alternative solutions can be quickly detected relative to the power flow solution time and corrected more than 80 % of the time.

Large-scale weather data remains essential for power grid and renewable energy research, yet accessing decades of ERA5 reanalysis data presents significant practical challenges due to inconsistent file structures and asynchronous API constraints. This work develops a parallel, restart-safe pipeline that automates the end-to-end process of downloading, cleaning, and storing 84 years of ERA5 single-level data for 27 variables on a $\mathbf{0. 2 5}^{\circ} \times \mathbf{0. 2 5}^{\circ}$ global grid. The pipeline processes each calendar day as an independent task, retrieving one GRIB file per day from the Copernicus Climate Data Store API and consolidating five internal datasets into a unified $\mathbf{2 4}$-hour structure. A centralized orchestrator coordinates two worker pools-thread-based for downloads and process-based for cleaning-while enforcing CDS rate limits and enabling fully idempotent restart behavior. Benchmarked on a 64 -core workstation with network-mounted storage, the parallel configuration achieves a $3.7 \times$ speedup over a minimally parallel baseline, reducing the projected 19402024 rebuild time from approximately 148 days to 40 days. These results demonstrate that maintaining a complete historical ERA5 archive is feasible on a single machine, enabling practical applications in large-scale analysis, database ingestion, and AI training workflows. The resulting database will support retrievalaugmented generation (RAG) systems for large language models (LLMs), providing contextual weather data for grid resilience analysis and decision support.

Electric vehicle (EV) charging can impose significant stress on distribution feeders, whose thermal and operational limits vary across space and time. This paper presents a linear programming (LP) method for estimating feeder-level EV hosting capacity using simulated demand data and publicly accessible feeder geometry and loading profiles. EV charging sites are geospatially mapped to feeders, baseline loading and hourly headroom are constructed, and a constrained $\mathbf{2 4}$-hour charging schedule is solved for each node subject to feeder, substation, hardware, and availability limits. LP is selected for its convexity, guaranteed optimality, and scalability to thousands of independent node-level problems. In contrast to approaches requiring detailed proprietary distribution system models, the proposed workflow uses only publicly accessible data sources, making it suitable for undergraduateled research and reproducible by the broader community. Case studies on California feeders demonstrate how headroom patterns influence achievable charging, reveal binding constraints, and highlight sensitivity to charging efficiency and maximum power.

This paper presents a prototype framework for developing domain-specific artificial intelligence applications in power systems analysis. An automated pipeline is demonstrated that transforms power system simulation files into structured databases, generates large-scale question-answer datasets, and implements retrieval-augmented generation with parameter-efficient fine-tuning. The system successfully converts power flow case files into SQLite databases, generates over 1000 natural language question-answer pairs grounded in simulation data, and enables interactive querying through a combination of retrieval-augmented generation and Low-Rank Adaptation finetuning of language models. The framework addresses the critical gap between specialized power systems knowledge encoded in simulation tools and the need for accessible, explainable AI-driven decision support systems in the energy sector.

Power system studies often involve large sets of operational data, simulation results, and technical documents that are not always straightforward to interpret quickly during disruptive events. This work presents a local language-model environment that uses retrieval-augmented search and targeted fine-tuning on power systems material to improve the clarity and relevance of model responses. The system indexes CSV and PDF data using a FAISS vector store for fast retrieval and can also run user-supplied Python scripts when a prompt requires additional analysis. A planner module determines which files or tools are necessary based on the user's question, enabling natural-language queries to trigger analytical workflows. The framework operates fully offline, supporting environments where data sensitivity and continuity are crucial. Early testing suggests that the approach can reduce manual effort and support situational awareness in hazard-resilient planning tasks.

Modern power system studies increasingly require transmission grid models that are not only electrically plausible but also physically consistent enough to support resilience and infrastructure-risk analysis. However, in many public synthetic cases, electrical parameters (R, X, B) and physical attributes (tower and conductor types, ratings, line lengths) are assigned largely independently, so thermal limits, impedances, and shunt terms do not always correspond to a realizable line design. This paper proposes a dual-path reconstruction framework that embeds standard transmission line physics and a library of typical ACSR conductors into synthetic models. Depending on which data are deemed more credible, the method treats either the thermal ratings (“Preserve MVA”) or the impedance set (“Preserve RXB”) as fixed, and then infers ACSR conductor size, bundle configuration, geometric mean radius (GMR), geometric mean distance (GMD), and line length to obtain a physically and electrically consistent design. Case studies on the EPIGRIDS 7k and ACTIVSg 2k Texas bus synthetic cases show that the reconstructed models closely match the original operating characteristics while supplying the additional design detail needed for tower- and weather-aware resilience studies. A validation was also conducted against the 2024 series ERCOT operating case.

This paper introduces a novel probabilistic framework for modeling weather-dependent load availability via an availability factor $A$, which represents the fraction of load remaining connected under adverse conditions. The method combines outage data with environmental parameters to estimate the probability distribution of $A$ conditioned on weather and geographic context. Using the EAGLE-I dataset for outage records and ASOS weather observations as a use case, we construct a spatiotemporally aligned dataset and train a machine learning model to classify availability into discrete outage severity bins. The resulting model enables the generation of stochastic load scenarios that capture both normal and outage conditions, providing a data-driven basis for probabilistic power flow and resilience studies. The proposed approach serves as a bridge between reliability analysis and load forecasting, allowing utilities and system planners to evaluate potential load reductions due to weather-related events and to integrate these scenarios into operational and planning models. The results for the United States in 2022 show a precision of up to 0.88, indicating robust predictive performance despite strong class imbalance.

Power system operators at control centers play a critical role in ensuring grid resilience and reliability during blackouts and other critical events. This paper introduces COGNIZANT, a next-generation human-machine interface platform that integrates advanced power system simulation with artificial intelligence-driven decision support and behavioral analytics. Developed jointly by Texas A&M University and the National Renewable Energy Laboratory (NREL), the platform addresses the critical knowledge gap created by retiring experienced operators while preparing the workforce for increasingly complex grid challenges. The platform combines PowerWorld Dynamic Simulator and GE's Advanced Energy Management System with large language models fine-tuned using Low-Rank Adaptation and retrievalaugmented generation to create an immersive training environment. It enables simulation of over $\mathbf{1, 0 0 0}$ diverse scenarios including extreme weather events leveraging historical ERA5 reanalysis data from 1940-2025, cyber-physical threats, and compound multi-domain events. The system is designed to achieve sub-second alert generation and targets a 20 % improvement in operator response times through comprehensive training validated using eye-tracking, cognitive load assessment, and behavioral analysis. This joint research effort establishes a closed-loop framework where NREL's ARIES platform generates high-fidelity grid scenarios and Texas A&M's Smart Grid Center evaluates operator performance, bridging the gap between grid physics and human factors to accelerate deployment of operator-centered resilience solutions.

This letter presents an algorithm that efficiently and consistently represents, in the positive-sequence power flow, the phase shifts introduced by wye–delta-connected transformers. As is well known, wye–delta transformers impose fixed voltage and current phase shifts in multiples of 30 degrees. Because these shifts do not affect bus voltage magnitudes or branch power flows, they are frequently omitted or modeled inconsistently. However, non-uniform representation of transformer phase shifts can introduce artificial voltage-angle discontinuities that negatively impact Newton-based power flow convergence. To address this issue, the letter introduces the concept of Phase Shift Groups (PSGs), which enforce consistent voltage-angle representation across the power system model. The proposed PSG-aware initialization yields a physically consistent starting point that improves Newton-Raphson convergence robustness compared to conventional flat-start methods. Beyond initialization, PSGs provide a systematic mechanism for data-quality validation, flagging incomplete or inconsistent transformer connection data. Scalability is demonstrated on systems with up to 110,000 buses with negligible computational overhead.

There are multiple mechanisms by which power lines can ignite wildfires. Because the conditions under which these mechanisms occur often coincide with factors that accelerate wildfire spread—particularly high winds—wildfires ignited by power-system equipment can rapidly become destructive and difficult to contain. In response, utilities implement a range of mitigation measures to manage this risk. This paper surveys the academic literature on mitigation strategies involving improved situational awareness, power shutoffs, and system hardening.

In this article, we propose a strategy to model the required spatiotemporal charging demand from light-duty (LD) and medium- and heavy-duty (MHD) electric vehicles (EVs) using actual transportation data by mapping the demand for the required EV charging to a realistic and coordinated distribution and transmission electric grid at the predicted times of the day to study their impact on the power system in a variety of load, weather, and EV penetration scenarios. This work is the first study that includes the actual weather data and transportation data with realistic and coordinated distribution and transmission grid data in a large industry-scale level study. The main goal of this study is to identify possible issues and required upgrades in the electric grid, caused by an increase in EV integration. The transmission case study is a large grid with 6717 buses over a Texas footprint, and the distribution grid is over Houston, a city in Texas, covering over three million customers. The resulting overloads and voltage violations experienced in the system are discussed, and required planning upgrades to avoid these issues are suggested.

This study develops an integrated approach that includes EV charging and power generation to assess the complex cross-sector interactions of vehicle electrification and its environmental impact. The charging load from on-road EV operation is developed based on a regional-level transportation simulation and charging behavior simulation, considering different EV penetration levels, congestion levels, and charging strategies. The emissions from EGUs are estimated from a dispatch study in a power grid simulation using the charging load as a major input. A case study of Austin, Texas is performed to quantify the environmental impact of EV adoption on both on-road and EGU emission sources at the regional level. The results demonstrate the range of emission impact under a combination of factors.

This paper presents a comparative analysis of renewable energy power output using forecast weather with different margins and historical weather data as benchmarks for selected days. The analysis evaluates the accuracy and performance trends of solar and wind forecasts against historical data, focusing on uncertainties at various forecast horizons. The benchmark hourly power generation data is used to compute relative errors, providing insights into temporal variations. Visualizations highlight the alignment between forecast trends and historical patterns, qualitatively assessing weather impacts on generation. The results for wind generation indicate that a seven-day forecast can achieve an accuracy of 80 percent, while a five-day forecast can reach an accuracy of approximately 90 percent. However, forecasts beyond ten days are only about 50 percent or less accurate compared to actual data. In contrast, the results for solar generation show comparable levels of accuracy throughout the entire forecast date but with higher average error values. This information is useful for grid operation, planning, electricity market, reliability and resilience studies and energy management.

This paper discusses results of an informal survey intended to identify the most impactful electric power system papers from 1975 to 2024 written in English. The survey was shared primarily over email to a large number of electric power engineers asking them to identify up to three papers that they consider among the most impactful papers of the last 50 years. A total of 144 valid responses were received, identifying 101 unique publications. Of those publications, 18 received multiple votes. The paper receiving the most votes are in the areas of electricity markets, optimal power flow, synthetic electric grids, voltage phasor measurements, electric grid stability, and associated with the open-source power system analysis program. This paper is a follow-up to a 2000 paper identifying the top papers of the 20th century.