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At least 73 records · Page 4

Methods and Tools To Assess Robustness of Nuclear Plant Outages

Refueling outages are one of the most challenging phases in a nuclear power plant (NPP) operating cycle. Refueling outages are extremely costly for an NPP due to the large amount of required resources and because of lost revenue due to plant being off the grid. Outage durations have steadily decreased across the industry over that last few decades primarily due to improved planning and coordination, but there are still many plants that struggle to meet the performance metrics accomplished by other utilities. Schedule resilience is one of the issues. NPP outages require scheduling thousands of activities within 30 days on average. Despite detailed planning, once the outage starts, numerous emergent issues typically appear along with schedule delays requiring continuous replanning and adjustment. When schedule disruption occurs during an outage, plant staff make urgent efforts to recover but are often not able to maintain the planned outage duration. These outage delays can cost a utility several million dollars per day. Tools that could help outage schedulers create a more resilient schedule and allow them to optimally reschedule emergent work could significantly reduce outage delays. One key aspect of creating a resilient schedule is to have accurate estimates for activity duration. Another important outage scheduling capability is the ability to schedule emergent work with minimal disruption. This paper focuses on developing tools and methods to support NPPs with outage schedule optimization and it describes the initial development of tools to support outage management that leverage computational and machine learning methods.

97 - MATHEMATICS AND COMPUTING↗

PR100: Estimated Medium- and Heavy-Duty Electric Vehicle Adoption and Load Estimation in Puerto Rico through 2050

The 2-year Puerto Rico Grid Resilience and Transition to 100% Renewable Energy Study analyzed stakeholder-driven pathways to Puerto Rico’s clean energy future. Outputs relating to electricity demand modeling were partially informed by estimates of electric vehicle adoption across all classes of medium- and heavy-duty vehicles (MHDVs), and the ensuing charging loads. To create these estimates, the team developed a transportation model for MHDVs in Puerto Rico to estimate the amount and geospatial distribution of energy used. Charging schedules for the different end uses of MHDVs were then used to construct electric load shapes assuming a portion of those vehicles would be replaced by battery electric counterparts. Study results showed that, by 2050, electric vehicles may constitute roughly 50% of the MHDV population in Puerto Rico. The resulting electrical demand curve attributable to MHDV charging showed that, for solar energy-based electrical systems with limited energy storage, this demand may create challenges unless appropriately managed either on the demand or supply side.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

Abstract With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗

Modeling Electric Vehicle Charging Load Using Origin-Destination Data

The accelerating adoption of electric vehicles (EVs) poses challenges to the power grid, necessitating precise representation of mobility patterns for effective infrastructure upgrades. Traditional simulation-based charging demand estimation faces limitations in generating trip chains reflective of actual travel patterns without complex network modeling. Hence, an innovative agent-based trip chain generation model is introduced to overcome these challenges. Drawing from the National Household Travel Survey (NHTS) and the NextGen NHTS origin-destination add-on data for Clarke County, Georgia, this study proposes a simulation method capturing both temporal and spatial mobility patterns without relying on extensive network topology data. The resulting trip chains predict EV charging load at the Census Block Group level, validated with a 1.03 correlation to actual trip counts, affirming their reflective accuracy. Two charging scenarios, residential-only and charging-everywhere, reveal distinct demand profiles. The charging-everywhere scenario aligns closely with the trip profile, while the residential-only scenario exhibits an afternoon peak slightly surpassing the former. This study contributes a data-driven charging demand estimation methodology, offering critical insights for grid resiliency planning amid the evolving landscape of EV adoption.

Pan, Melrose↗

Nexus of thermal resilience and energy efficiency in buildings: A case study of a nursing home

Extreme weather events become more frequent and severe due to climate change. Although energy efficiency technologies can influence thermal resilience of buildings, they are traditionally studied separately, and their interconnections are rarely quantified. This study developed a methodology of modeling and analysis to provide insights into the nexus of thermal resilience and energy efficiency of buildings. In this work, we conducted a case study of a real nursing home in Florida, where 12 patients died during Hurricane Irma in 2017 due to HVAC system power loss, to understand and quantify how passive and active energy efficiency measures (EEMs) can improve thermal resilience to reduce heat-exposure risk of patients. Results show that passive measures of opening windows and doors for natural ventilation, as well as miscellaneous load reduction, are very effective in eliminating the extreme dangerous occasions. However, to maintain safe conditions, active measures such as on-site power generators and thermal storage are also needed. The nursing home was further studied by changing its location to two other cities: San Francisco (mild climate) and Chicago (cold winter and hot summer). Results revealed that the EEMs' impacts on thermal resilience vary significantly by climate and building characteristics. The study also estimated the costs of EEMs to help stakeholders prioritize the measures. Passive measures that may not save energy may greatly improve thermal resilience, and thus should be considered in building design or retrofit. Findings from this study indicate energy efficiency technologies should be evaluated not only by their energy savings performance but also by their influence on a building's resilience to extreme weather events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Virtual Power Plants: Pilots, Challenges, and Innovations Shaping Future Development

Virtual Power Plants (VPPs) aggregate distributed energy resources (DERs) to provide grid services traditionally delivered by centralized power plants. This article reviews the current state of VPP deployment, highlighting business models, compensation mechanisms, and global pilot projects. While VPPs offer benefits such as grid flexibility, resilience, and cost savings, challenges remain in communication infrastructure, regulatory frameworks, market access, and customer engagement. To address these, we propose a scalable, privacy-preserving hierarchical VPP architecture that coordinates with distribution utilities and preserves customer data. We also present the Integrated T&D Control Room of the Future as a key test bed for validating and accelerating VPP adoption. These innovations can help transition VPPs from pilot programs to integral components of a modern, reliable power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper presents the analysis which help visualize the DER impact on the grid, identify the challenges and provides an insight into the new distribution management and control needs to enable reliable and resilient grid operations.

behind-the-meter↗

KS4A-IsoG.1.0_FspDS682

The novel fungal strain, Fusarium sp. DS 682, was isolated from the rhizosphere of the perennial grass, Bouteloua gracilis, at the Konza Prairie Biological Station in Kansas. This fungal strain is common across North American grasslands and is resilient to environmental fluctuations. The draft genome is estimated to be 97.2% complete.

54 ENVIRONMENTAL SCIENCES↗

Valuing Resilience for Microgrids: Challenges, Innovative Approaches, and State Needs

The United States depends on the delivery of reliable, affordable, clean, and safe electricity. Electric utilities invest billions of dollars each year in generation, transmission, and distribution assets to meet this need. However, experiences with recent natural disasters of increasing frequency and duration demonstrate the shortcomings of this approach in the face of modern threats. Further, as customers rely on electricity for a broader range of important needs, such as transportation, as well as critical life-saving services and mission critical facilities such as water treatment, medical care, shelters, telecommunications, and more, the need to minimize the likelihood and impacts of outages grows. Against this backdrop, resilience has emerged as a key consideration to guide electricity spending, whether from utilities, customers, or taxpayers. Although reliability has been defined and measured for decades with broadly accepted metrics that measure how many customers lose power and at what frequency and duration, resilience considers the electricity system’s response to a disruption and its subsequent impacts on customers. Developing tools and methods to accurately assess the costs and benefits of resilience investments is a critical step toward the goal of mitigating the impacts of outages on customers and society. Today, electric system resilience is largely treated as an externality due to challenges estimating the costs of long-duration outages, impacts of outages on society, and increasing reliance on electricity for a growing set of interdependent services. These interdependencies include the water, wastewater, telecommunications, natural gas, and health sectors. Without knowing how much a given resilience investment will benefit customers or society more broadly, investors, policymakers, and regulators are less likely to make or approve such investments, and less able to prioritize those investments. State Energy Offices and public utility commissions (PUCs) lead the development of state-level energy policy and utility regulation, respectively, and each has interests in encouraging appropriate public and private investments in resilience. To this end, the National Association of State Energy Officials (NASEO) and National Association of Regulatory Utility Commissioners (NARUC), with the support of the U.S. Department of Energy (DOE) Office of Electricity (OE), formed a joint Microgrids State Working Group to explore the costs and benefits of microgrids, barriers to broader deployment of microgrids to meet resilience and other objectives, and policy and regulatory strategies to optimize investments in resilience, including but not limited to microgrids. Although no universally accepted valuation tool for resilience exists, National Laboratories, utilities, researchers, and state and federal agencies have collaborated to develop, apply, and improve a number of approaches to quantify resilience, several of which are still in progress at the time this report is published. This report seeks to share these important advances by discussing current definitions of resilience (Section 1), how microgrids are defined and used to meet resilience objectives (Section 2), new approaches to valuing resilience (Section 3), steps State Energy Offices and PUCs have taken to further resilience valuation efforts (Section 4), and finally, considerations and suggested next steps for State Energy Offices and PUCs (Section 5). Relevant examples of specific microgrid projects and resilience valuation efforts are included throughout the report. While this report is written specifically for NASEO and NARUC members, it may be useful for utilities, local governments, and individual customers interested in improving the way public and private dollars are spent to achieve resilience outcomes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

2024.03.13 Bushing Subcommittee presentation on Transformer Bushing Seismic Resilience: Design, Hazard, & Prevention Analysis [Slides]

Presents results from power grid simulations on synthetic grids that estimate the impact from power transformer failures due to earthquakes. In the best-case scenario, there is an 82.9% likelihood of load loss greater or equal to 51 MW. Methodologies are described as well as path forward to improving transformer resilience and, consequently, the resilience of the bulk power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

US building energy efficiency and flexibility as an electric grid resource

Buildings consume 75% of U.S. electricity; therefore, improving the efficiency and flexibility of building operations could increase the reliability and resilience of the rapidly-changing electricity system. We estimate the technical potential near- and long-term impacts of best available building efficiency and flexibility measures on annual electricity use and hourly demand across the contiguous U.S. Co-deployment of building efficiency and flexibility avoids up to 742 TWh of annual electricity use and 181 GW of daily net peak load in 2030, rising to 800 TWh and 208 GW by 2050; at least 59 GW and 69 GW of the peak reductions are dispatchable. Implementing efficiency measures alongside flexibility measures reduces the potential for off-peak load increases, underscoring limitations on load shifting in efficient buildings. Overall, however, we find a substantial building-grid resource that could reduce future fossil-fired generation needs while also reducing dependence on energy storage with increasing variable renewable energy penetration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Multi-Agent Simulation Based Framework for Power Restoration Time Estimation at Distribution Level

The growing frequency of power outages has prompted increased interest in developing a more resilient power grid that can quickly recover from weather-related damage. At the distribution level, power restoration is a complex, multi-stage process involving multiple response entities. Providing utility stakeholders, government regulators, and the public with information about outage duration and estimated time to restoration is crucial. The research employs a multi-agent simulation approach, which allows for the simulation of decision-making behaviors among different entities and the incorporation of various uncertainties. Specifically, the study uses the open-source simulation package Mesa-Geo in conjunction with the Python language and constructs a road network using the open-source network extension pgRouting for routing queries. The research design includes several experiments focused on Florida as a case study, comparing repair crew sizes, power outage numbers, and road damage scenarios. The findings could offer valuable managerial guidance on resource allocation in the restoration process.

Chen, Yang↗

Merefa Community Microgrid: Conceptual Design and Sequence of Operations [Slides]

A conceptual design is described for a community microgrid in Ukraine. Microgrid resources include solar photovoltaics, battery energy storage, and conventional natural gas fueled reciprocating engine generators. The conceptual architecture was informed by the microgrid developer, NREL subject matter experts, and the application of REopt, an NREL-developed software tool created for identification of least-cost combination of resources for achieving cost savings, resilience, and renewable energy goals. The report includes conceptual architecture, estimates of key summary financial metrics, and sequence of operations.

14 SOLAR ENERGY↗

Generative AI for Power Grid Operations

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids: Preprint

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. Different types of DERs such as Photovoltaics (PV), energy storage, electric vehicles (EVs), etc. have varying effects on the grid based on how and where they are deployed. Again, the DER deployment and its impact are network-dependent, while the traditional electric grids were not designed to host DERs. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper investigates the impacts of integrating a mix of DERs in the utility distribution network in Colorado, U.S. Firstly, BTM DERs at the residential scale including distributed PV; battery energy storage systems (BESS); heating, ventilation, and air-conditioning (HVAC) load; electric water heater (EWH) load; and EVs are modeled. The impacts of integrating these resources on the distribution network are evaluated by conducting time-series simulations for different scenarios considering different days to capture the worst-case conditions. Monte Carlo simulations are conducted to generate the realistic EV charging profile. The voltage issues, substation transformer loadings, and critical nodes in the network are identified with the incorporation of uncoordinated EV charging loads and other DER in the network. This analysis helps visualize the DER impact on the grid, identify the challenges, and provides an insight into the new distribution management and control needs to enable reliable and resilient distribution grid operations. Additionally, further analysis is performed to estimate the daily residential electricity cost with the inclusion of DERs under time-of-use tariffs. The result shows the daily residential electricity cost reduced by 20:7% on average with DERs compared to the case without DERs.

27 ARPA - Advanced Research Projects Agency-Energy↗

Incorporating climate change into risk-informed resilience planning

In response to the development of portfolio-wide Climate Action Plans by federal agencies, federal sites are working to incorporate the impacts of climate change into their resilience assessments. However, it can be challenging to incorporate climate change scenarios into resilience assessments given the uncertainty inherent in climate change modeling. Incorporating these factors into a resilience plan requires an understanding of what the different climate scenarios mean, as well as how to estimate potential impacts of climate change on hazard occurrence on a regional, or even local, scale under different scenarios. We discuss approaches to incorporating this data into risk-informed resilience assessment processes, such as those implemented in the Department of Energy’s (DOE) Federal Energy Management Program’s (FEMP) Technical Resilience Navigator (TRN) and the Sustainability Performance Division’s (SPD) Vulnerability Assessment and Resilience Plan (VARP) Risk Assessment Tool. We also describe the climate scenarios and the availability of hazard data for site resilience planning, based on modeling included in the International Panel on Climate Change (IPCC), the National Climate Assessment (NCA), and state-level reports. We present examples from the TRN risk assessment and the VARP Risk Assessment Tool to illustrate how sensitivity analysis can be used to incorporate climate change projections into the resilience planning process.

Rabinowitz, Hannah S.↗