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DOE OSTI2022

Exceedance Probabilities and Recurrence Intervals for Extended Power Outages in the United States

Power outages cause significant economic and societal impacts. An increasing likelihood of extreme weather coupled with aging grid infrastructure is leading to a higher prevalence of extended power outages, which can leave customers without power for multiple days or even weeks. Planners at the facility, local, state, and federal levels are interested in resilience solutions to reduce the impacts of extended power outages. Resilience solutions—such as installing backup systems, integrating microgrid solutions, weatherizing buildings, and hardening distribution and transmission components—can reduce the consequences of extended power outages, but these solutions come with increased capital costs. Conducting cost-benefit analyses is important for determining which steps to take to mitigate the impact of power outages without investing in ineffective and cost-prohibitive resilience solutions. The expected benefits of resilience investments depend on the frequency of power outages of various durations, particularly extended outages lasting several hours, days, or weeks. A significant barrier to resilience planning is the lack of publicly available data on the frequency and duration of extended power outages. The absence of outage duration information severely limits the ability to conduct quantitative cost-benefit analyses of resilience investments. This report provides estimates of recurrence intervals and conditional exceedance probabilities for major power outages by U.S. region between 2015 and 2021. Additionally, we provide estimates for grid management, particularly outages caused by California’s public safety power shutoffs (PSPS), and for natural outages caused by major hurricanes. Outage recurrence intervals are the average number of years between outage events, and conditional exceedance probabilities are the likelihoods that a customer who experiences a major power outage will experience an outage exceeding a given duration. Major outage events are those that affect 10,000 or more customers, as defined by the U.S. Department of Energy’s (DOE’s) Electric Emergency Incident and Disturbance Report, called OE-417 (DOE 2020). These results can be applied to determine the likelihood of experiencing long-duration outages, which can be integrated into cost-benefit analyses of resilience solutions and broader energy resilience studies. We developed a methodology to estimate customer outage durations for extended outage events using publicly available data on customer outages. Shorter-duration power outages are relatively common, with customers experiencing on average more than one power outage each year lasting fewer than 12 hours. An outage event lasting between 1 day and 1 week is expected to occur between once every 16 years and once every 42 years, depending on the region, with an average recurrence rate of once every 32 years across the contiguous United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Powering Up Secure and Reliable Operations at the Port of Honolulu's Kapalama Container Terminal

The National Laboratory of the Rockies (NLR) is providing resilience planning support to the Pasha Group, the operator of Kapalama Container Terminal (KCT) at the Port of Honolulu, by exploring how distributed clean energy resources could help the terminal sustain a 36-hour outage and continue emergency operations. The Department of Energy's Energy Transitions Initiative (ETI) aims to advance self-reliant island and remote communities by promoting resilient, affordable, and sustainable clean energy resources.

25 ENERGY STORAGE↗

DOE OSTI2024

Data Analytics and Visualization of Energy Systems for Critical Infrastructure Insights

Modernization of energy systems including transportation facilities provides opportunities for increased efficiency, expansion of commerce and meeting industry and federal goals. A significant increase in electrical demand is projected to meet these needs, which concentrates at facilities such as airports. For example, Xcel Energy working with two airports in their service area recently published information projecting an up to fivefold increase in electricity demand in the next 25 years [1]. Concurrently, the US Government Accountability Office (GAO) recently surveyed 30 commercial service airports identifying more than 300 outages of more than 5 minutes between 2015 and 2022 [2]. Power, reliability, and resilience planning becomes more important to safely maintain operations and the flow of commerce with fewer energy carriers providing necessary energy to safely move passengers and goods. NREL proposes to develop methodologies to allow owners, utilities, and federal agencies to dynamically analyze, forecast, and manage energy loads at airports, focused upon maintaining the flow of commerce in an efficient, sustainable, and resilient way. To address these energy challenges, a suite of technologies and methodologies can be leveraged to validate concepts, inform design, de-risk solutions and optimize energy management during deployment. These technologies include digitalization of energy systems, microgrid methodologies, and related energy technologies for building and vehicle loads. [1] Electrifying Airport Ecosystems - https://www.enterprisemobility.com/content/dam/enterpriseholdings/marketing/innovation-in-mobility/vehicle-innovation/airport-electrification-study-full-report-2024.pdf [2] Airport Infrastructure: Selected Airport's Efforts to Enhance Electrical Resilience https://www.gao.gov/products/gao-23-105203.

critcal infrastructure↗

DOE OSTI2022

Advanced Technology and a Resilient Electric Grid

Making the electric grid cleaner and more resilient will require investing in a variety of new and emerging technologies. Advanced technologies including hydrogen, small modular nuclear reactors, and energy storage will all have a role to play in modernizing the electric grid. A diverse energy supply helps to keep the lights on and protects critical infrastructure, however it will also be important to assess the potential vulnerabilities and risks that come with unfamiliar and new technologies. This webinar will feature experts on resilience planning around advanced technologies from multiple sectors and share resources for states as they seek to modernize and decarbonize their electric grid.

advanced technology↗

DOE OSTI2021

Ahtna Tribal Energy Planning and Technical Assistance Project (Final Report)

Ahtna Intertribal Resource Commission (AITRC) provided technical assistance and capacity building activities for eight (8) tribal communities in the Ahtna region to: 1. reduce energy costs; 2. improve community wildfire readiness and biomass utilization; 3. provide local food security; and 4. reduce unemployment, poverty and outmigration in the Ahtna villages. AITRC provided community education on energy needs, conservation and efficiency, issues and options, assisted the tribes in identifying appropriate renewable energy systems, and increased their capacity to implement successful energy projects that employed local tribal members. AITRC also successfully assisted the member Tribes and Alaska Native Corporations in pursuing additional funding to advance their energy goals and projects, including Tribal Resiliency Planning, community biomass heating and wood resource assessment projects, and hydroelectric.

09 BIOMASS FUELS↗

DOE OSTI2024

Invasive Plant Species Management Plan for Los Alamos National Laboratory (Rev.3)

Native species are plants and animals that continually occupy a natural range without direct or indirect introduction and/or care by humans. They are adapted to the environmental conditions and processes of the ecosystem in which they reside. A species introduced into a novel ecosystem can either exploit that ecosystem and thrive or be unable to survive in that ecosystem (Hobbs et al. 2006). Alien or non-native species are species that are intentionally or accidentally introduced into a novel ecosystem and are capable of living and propagating within the physical parameters of that ecosystem. Invasive species is a species that is non-native (or alien) to the ecosystem under consideration and a noxious species are those whose introduction causes or is likely to cause economic or environmental harm. The term invasive species is applicable to plants and animals alike; however, this invasive species management plan currently focuses on invasive vegetation. Invasive plant species are usually capable of rapid colonization of disturbed ground, such as after a change in wildfire regime intensity and frequency (Reilly et al. 2020) or anthropogenic ground disturbance (Burke and Grime 1996; Hobbs and Huenneke 1992). Climate change facilitates the spread and establishment of many alien species and creates new opportunities for them to become invasive (Turbelin and Catford 2021). As climate change impacts increase in the coming decades, we may see in an increase in invasive species establishment. See a list of definitions of terms pertinent to this document in Appendix A: Definitions. Los Alamos National Laboratory (LANL) hosts populations of non-native and invasive species all typical of the northern New Mexico region (Martin 2004; NMDA 2020). By implementing an invasive species management plan, LANL will have readily accessible management strategies for invasive species that are found on-site. The benefits of managing invasive species include a decrease in wildland fire risks, an increase in soil productivity, an increase in (productive or beneficial) wildlife habitat, an increase in water quantity and quality, and the restoration of impacted areas (Burke and Grime 1996; Hobbs and Huenneke 1992; D’Antonio and Hobbie 2005; MacDougall et al. 2013; Reilly et al. 2020). The aforementioned benefits from invasive species management directly enable the LANL mission by ensuring compliance requirements are met and site-wide programs, such as the Vulnerability Assessment and Resilience Plan, are supported for a mutually beneficial outcome. An example for the LANL site specifically, controlling non-native annual plant species, for example, could reduce the costs associated with stabilizing soils during the stormwater pollution prevention compliance process. Roadway and utility right-of-way areas are another place where an integrated vegetation management strategy would promote low growing perennial plants in a way that is mutually beneficial to habitat and the institution through lowered maintenance costs. There is also an economic benefit to managing invasive species. In one nationwide study, invasive plants had an estimated impact cost of $190.45 billion (Fantel-Lepczyk et al. 2022). Investing in preventative measures and surveillance could help to offset future control and management costs of invasive species that have the potential to become established. The State of New Mexico has developed plant species lists and recommendations through the New Mexico Department of Agriculture’s Noxious Weeds Management Act, Article 7D (NM Statute § 76-7D-4 2021); however, effective invasive species management must rely on local knowledge of the site and region. Los Alamos County (LAC) has already compiled a target invasive plant species list and species-specific management objectives (Martin 2004). Therefore, the New Mexico Noxious Weed List and the LAC invasive plant species list, as well as management objectives from those documents, are integrated into LANL’s invasive plant species management plan.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2023

Unalakleet Climate: Analyzing Permafrost Degradation and Drainage Networks in Unalakleet, Alaska

The coastal community of Unalakleet is currently the 8th most at-risk community in Alaska due to the adverse effects of climate change that include permafrost degradation, severe coastal erosion, and sea-level rise-induced flood inundation caused by increasingly frequent storm surges. In response, the community has started a managed relocation with support from the Native Village of Unalakleet (NVU) and the National Renewable Energy Lab (NREL)’s Alaska campus in Fairbanks. The Unalakleet Climate NASA DEVELOP team partnered with NREL to provide remote sensing support and analysis for resilience planning in Unalakleet, supporting their ongoing relocation efforts and guiding future expansion. The team utilized Sentinel-1 C-Synthetic Aperture Radar (SAR), WorldView-2, and WorldView-3 datasets from 2017 – 2023 to analyze seasonal summer subsidence and utilized a 2014 Ancillary USGS 5 m Alaska DEM to conduct drainage network analyses that included watershed delineation and Height Above Nearest Drainage (HAND) analysis. The team also used high-resolution WorldView images to locate stable reference points that served as quality control for the team’s analyses. The team’s end products included maps containing subsidence and drainage zones information at and surrounding the relocation site. The team’s products provide NREL with valuable data that enables them to better assist Unalakleet’s managed relocation and assists Unalakleet with adapting to the catastrophic effects of climate change and build resilience in a community on the front lines of climate change.

Ian Lee↗

DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Mitigating urban climate-energy feedback with citywide building-integrated photovoltaics implementation

Urban heat islands (UHIs) intensify cooling demands, whose waste heat further exacerbates UHI, creating a self-reinforcing feedback loop. Building-integrated photovoltaics (BIPV) can break this cycle by generating electricity and altering urban climate-energy interactions, yet its net impact remains unresolved. Using a cross-scale modeling framework for 118,521 buildings in Hong Kong, we demonstrate that BIPV glazing reduces cooling demand through improved insulation and lower solar heat gain, while inducing diurnal thermal asymmetry of daytime warming (up to +1.9°C) and nighttime cooling (up to −2.5°C). This microclimate regulation indirectly contributes to a 0.6% reduction in citywide building energy use. Cumulative savings from electricity generation and reduced energy use reach 4.7% (2,060.2 GWh) and 10.1% (4,401.3 GWh) under low- and high-coverage deployment, respectively, with building-level savings of −20.1% ± 11.7% (mean ± SD) at high coverage. We establish BIPV as a configurable climate-energy regulator that enables net-zero, heat-resilient planning and quantifies deployment trade-offs in cooling-dominated cities worldwide.

Building-integrated photovoltaics (BIPV)↗

DOE OSTI2026

An Integrated Modeling Framework for Sediment Dynamics During Urban Flooding: Application to Hurricane Harvey in Houston

Floodwater can mobilize and redistribute large volumes of sediment from upland to downstream urban areas, threatening infrastructure, water quality, and ecosystem health. However, existing modeling approaches often fail to capture sediment dynamics in urban floodplains due to the lack of integration between upland hydrological processes and riverine sediment transport. This study presents the first integrated modeling framework that couples the Energy Exascale Earth System Model (E3SM) land component, which simulates runoff and hillslope erosion, with TELEMAC-GAIA, a two-dimensional hydrodynamic and sediment transport model. This framework enables the fully distributed, process-based simulation of high-resolution (as fine as 30 m) sediment dynamics from hillslopes to floodplains. Applied to a highly urbanized watershed in Houston during Hurricane Harvey, this framework reproduced observed water levels at 16 USGS gauges (median R 2 = 0.83 and KGE = 0.78), key sediment dynamics such as sediment transport and deposition processes, and reproduced spatial deposition patterns consistent with LiDAR-derived data. Based on the simulation, we estimate 8.0 million m 3 of event-scale sediment deposition, including 5.7 million m 3 trapped in the flood-control reservoirs and 2.3 million m 3 deposited along major channels and floodplains. Using a representative unit removal cost, this corresponds to an estimated dredging cost of $581 million for total deposition. These results provide a first-order, physically based quantification of Harvey-scale sediment impacts. This study provides a valuable tool for the holistic analysis of sediment dynamics triggered by extreme urban flooding, supporting flood-resilience planning. More broadly, it highlights the importance of integrating physically based hydrological processes for urban flooding and sediment research.

Hurricane Harvey↗

DOE OSTI2024

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↗

DOE OSTI2025

Comparing multi-source urban flood indicators: satellite, simulation, and citizen-reported data

Urban flooding arises from complex mechanisms, making it challenging to capture accurately with a single detection method. This study evaluates three complementary approaches to detect flooding across three Chicago neighborhoods: (i) Sentinel-1 synthetic aperture radar (SAR), offering weather-independent, high-resolution (10 m) imagery of surface inundation; (ii) the storm water management model (SWMM), simulating combined sewer overflow and drainage performance; and (iii) citizen-generated 311 service requests, capturing observed flooding impacts. By analyzing six storms ranging from severe to mild, we examine how each source uniquely contributes to identifying urban flood events. SAR imagery effectively identifies standing water but can miss brief flooding due to satellite revisit constraints. SWMM provides detailed insights into system-wide drainage behavior yet may underestimate localized street-level flooding. Meanwhile, 311 calls reflect real-world flooding impacts but are vulnerable to underreporting. Statistical overlap analysis highlights chronic flood hotspots repeatedly identified across multiple detection methods, indicating persistent infrastructure and topographic vulnerabilities. Temporal analysis further reveals that while SWMM flooding aligns closely with rainfall peaks, 311 calls typically precede or persist beyond these peaks. Our findings emphasize the value of using satellite observations, hydrological modeling, and resident-reported data in a complementary manner to better interpret patterns in flood timing, severity, and spatial distribution—providing insights that can inform targeted infrastructure improvements and contribute to urban flood resilience planning.

311↗

DOE OSTI2025

HexWeather: Hexagonal Spatial Data Aggregation for Weather-Driven Grid Resilience Analysis

Extreme weather accounts for over 8 0 % of major U.S. power outages since 2000, highlighting the need for spatial tools that align weather data with the irregular boundaries of electric infrastructure. This paper introduces HexWeather, a modular, resolution-aware framework for aggregating historical and forecasted weather data using Uber's H3 hexagonal spatial indexing system. Unlike traditional methods that rely on state or county-level grids, HexWeather enables weather analysis across custom geographies such as utility service areas where public datasets are often unavailable or misaligned. Using Open-Meteo data, we evaluate how H3 resolution affects anomaly detection, spatial variability, and forecast uncertainty across three scales: state, county, and utility. Results show that while coarse resolutions suffice for broad trend tracking, finer resolutions are essential for identifying localized variability and operational risks. By applying metrics like Z-score standard deviation and interquartile range, HexWeather quantifies the spatial spread of both historical anomalies and forecasted conditions, allowing users to assess resolution adequacy for each analysis. This framework supports rapid weather data reuse, reproducible anomaly detection, and predictive modeling for infrastructure resilience. By bridging spatial misalignment in traditional datasets and enabling retrospective and forward-looking analysis within the same pipeline, HexWeather lays the groundwork for better post event analysis, outage prediction, and resilience planning.

Morris, Jacob [ORNL]↗

DOE OSTI2023

Resilience Development For Electric Energy Delivery Systems

The Resilience Development for Electric Energy Delivery Systems (ResDEEDS) tool walks users through the process of evaluating electric energy delivery systems (EEDS) for resiliency. It implements the steps of the INL Resilience Framework for EEDS and provides automated tracking of resilience planning and suggestions for mitigating hazards.

Culler, MeganJ.↗

DOE OSTI2025

sup3ruhi (Super Resolution for Renewable Resource Data and Urban Heat Islands) [SWR-25-05]

Urban heat is a growing concern, particularly in dense metropolitan areas where high temperatures increase the risk of heat-related illness and drive energy expenses for cooling. Estimating the effects of urban heat remains a challenge due to limitations in describing the built environment, computational constraints, and the need for high-resolution data. This software presents open-source, computationally efficient machine learning methods that enhance the accuracy of urban temperature estimates compared to historical reanalysis data. Models trained using this software have been applied to urban microclimates in Los Angeles and Seattle showing greater accuracy and less bias when compared to low-resolution reanalysis datasets like ERA5 and even when compared to high-resolution mesoscale numerical weather models like WRF with an urban canopy model. Initial findings highlight how machine learning can support urban heat resilience planning by enabling improved assessments of local heat islands, mitigation strategies, and their energy implications. This software is an extension of (sup3r). This software supports the following publication: Buster, Grant, et al. Tackling Extreme Urban Heat: A Machine Learning Approach to Assess the Impacts of Climate Change and the Efficacy of Climate Adaptation Strategies in Urban Microclimates. arXiv:2411.05952, arXiv, 8 Nov. 2024. arXiv.org, https://doi.org/10.48550/arXiv.2411.05952. And has related public data records available at: Buster, Grant, Cox, Jordan, Benton, Brandon, and King, Ryan. Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI). United States: N.p., 16 Oct, 2024. Web. https://data.openei.org/submissions/6220.

Buster, Grant [National Renewable Energy Laborator↗

DOE OSTI2021

FY2020 Technology Integration Annual Progress Report

The 2020 Technology Integration Annual Progress Report covers 46 multi-year projects funded by the Vehicle Technologies Office. The report includes information on competitively awarded projects, ranging from rural shared mobility demonstration projects to medium- and heavy-duty electric vehicle deployment to statewide alternative fuel resiliency planning. It also includes projects conducted by several of the Vehicle Technologies Office’s (VTO) National Laboratory partners, Argonne National Laboratory, Oak Ridge National Laboratory and the National Renewable Energy Laboratory. These projects range from a Technical Assistance project for business, industry, government and individuals, to the EcoCar Advanced Vehicle Technology Competition, and the Fuel Economy Information Project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Water Microgrids: A Primer for Facility Managers

A water microgrid is similar in concept to an energy microgrid, which the U.S. Department of Energy defines as “a local energy grid with control capability, which means it can disconnect from the traditional grid and operate autonomously.” Similarly, a water microgrid is a local water system that supplies, treats, and distributes water, with the primary objective to meet mission critical water demands during a disruption of the primary supply. A water microgrid has the capability to operate independently of an existing primary water system and includes a layer of sensing capability that provides the necessary monitoring and controls to operate the water microgrid. A water microgrid may offer a viable solution for a site to address resilience gaps identified through the resilience planning process. For example, a site may not have adequate and redundant water supply to meet mission critical demands with vulnerabilities in the operating systems. A water microgrid provides the ability to island the water system from the primary water supply to satisfy water demand requirements throughout an outage or disruption.

42 ENGINEERING↗

DOE OSTI2023

Assumptions and advantages of diagnosing climate change at global warming levels

Scientists increasingly assess climate changes at specific levels of global warming rather than at specific times in the future. This has numerous advantages over other methods of diagnosing future climate changes and allows for a more direct connection with various climate mitigation targets like the 1.5° or 2°C Paris agreement goals. A key assumption inherent to global warming levels (GWLs) is that all climate models and all future experiments that cross a given global temperature change threshold can be averaged together – that the response of the climate at a given GWL is largely independent of the emissions scenario or the sensitivity to carbon dioxide in the models comprising the average. This assumption has not been systematically vetted in the literature. Here we mine the entire CMIP5 and CMIP6 archives to determine the extent to which the climate response of several extreme climate indices at various GWLs is sensitive to the future emissions scenario and to the models’ climate sensitivities. We quantify this sensitivity to see whether using GWLs as a control in climate projections is justified. This information will be valuable for establishing the extent to which constraints on climate sensitivity can have tangible benefits for regional climate resiliency planning.

58 GEOSCIENCES↗