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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 OSTI2022

Hardware-Based Randomized Encoding for Sensor Authentication in Power Grid SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems.

42 ENGINEERING↗

DOE OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Energy and Power Evolution Over the Lifetime of a Battery

Li-ion batteries currently are dominant energy storage devices for electric vehicles. Rechargeable batteries with lower cost, longer lifetime, and higher safety are desired in support of building of a green grid infrastructure. The continued investment in new battery materials, novel battery structures, advanced manufacturing processes, and accelerated testing/validation of battery performance has led to significant progress in battery development and deployment. Battery safety/reliability, which is essential to the success of a battery technology in the real world, naturally becomes the next big topic in battery research. Recently, the increasing interest in long-duration storage, fast charging, battery secondary use, and material recycling to build a circular industry and sustainable material supply chain has compelled further attention to understand the energy/power evolution and safety over the lifetime of a battery. Here, in this short Viewpoint, we discuss some high-level analyses on the energy/power evolution of rechargeable batteries over their life cycles aiming to inspire more discussion on the safety and sustainability of some representative and emerging battery technologies.

25 ENERGY STORAGE↗

DOE OSTI2023

Inferring adversarial behaviour in cyber‐physical power systems using a Bayesian attack graph approach

Abstract Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.

Sahu, Abhijeet↗

DOE OSTI2025

A Novel Machine Learning Workflow to Classify Mobile Home Parks at Scale

Understanding the built environment is essential to the overall study of population dynamics, grid infrastructure, emergency response, among others. In the United States there are multiple classifications for buildings within the built environment such as residential, signifying family homes while commercial buildings consist of apartments or larger structures which are multi-purpose. While there is a high level of understanding of where these aforementioned structures are located, there is a third class of structures, mobile home parks (MHP) which have been under-represented in the literature despite there being an estimated 2.7 million of them within the United States. Research has shown that individuals who reside in MHP are at higher risk to extreme events due to their location and structural integrity of residence. Attention must now turn to identifying MHP at scale to help first responders and policy makers understand where these at risk populations reside. To address for this gap, we develop a novel methodology to infer MHP at scale based off morphologies derived at a building level. Here, we show that across 3 million buildings in 6 states within the United States it is possible to identify MHP with 83% accuracy. This novel approach to identify MHP from other structures within the built environment using a machine learning approach provides a new tool to leverage in relation to helping at-risk populations.

Stipek, Clinton [Oak Ridge National Laboratory (OR↗

DOE OSTI2021

Efficient Contingency Analysis in Power Systems via Network Trigger Nodes

Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such ’what-if’ scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k ‘trigger’ components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.

Tabassum, Anika↗

DOE OSTI2026

Oil-impregnated densified wood veneer with high electrical insulation enabled by nanosized oil channels

Growing energy demands and renewable integration are stressing the aging power grid infrastructure. Lignocellulosic oil-impregnated paper is widely used in power transformers but suffers from critical limitations, such as low dielectric strength, mechanical strength, and thermal conductivity, causing premature transformer failures. Here, we demonstrate a superior electrically insulating oil-impregnated paper design using the naturally anisotropic structure of densified wood veneer to achieve nanosized channels of oil that efficiently disrupt electrical breakdown pathways. The developed oil-impregnated densified wood (ODW) creates aligned cellulose fibers with 166 ± 87–nanometer oil nanochannels, achieving record dielectric strength of 105 kilovolts per millimeter. The structure also delivers a mechanical strength of up to 384 megapascals and a thermal conductivity of 0.33 watts per meter per kelvin, enabling enhanced longevity upon thermal aging tests. The ODW could replace conventional transformer insulation to enhance power transformer performance and improve lifetime. Moreover, its anisotropic oil-filled nanochannel design offers a general strategy for hybrid dielectrics in medium- and high-voltage applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

reV (The Renewable Energy Potential Model - Open Source) [SWR-21-59, SWR-20-20 and SWR-17-34]

The Renewable Energy Potential (reV) model is a platform for the detailed assessment of renewable energy resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP), and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for the assessment of resource potential, technical potential, and supply curves at varying levels of detail. The platform runs on the National Renewable Energy Laboratory’s (NREL’s) high-performance computing system, providing scalable and efficient performance from a single location up to a continent, for a single year or decades of time-series resource data. Coupled with NREL’s System Advisor Model (SAM), reV supports resource assessments from 5-minute to hourly temporal resolutions and supports the analysis of long-term (i.e., year-on-year) variability of renewable generation (e.g., interannual variability and exceedance probabilities).

Maclaurin, Galen↗

DOE OSTI2026

GridPIQ Reference Data

GridPIQ uses dozens of publicly available datasets to provide context for a user's grid project, as well as defaults for users to choose from. Users can choose to import their own data to better customize their analysis or use GridPIQ-supplied defaults. This allows users to get up and running with an analysis very quickly without having to spend significant time pulling together input data. To run an electric vehicle (EV) smart charging project, a user will need to provide or select from prepopulated values for the regional load profile shape and peak load, region of interest and closest weather station, EV charging profile, number of EVs to add for the analysis, maximum EV charging power, location of chargers relative to grid infrastructure, and allowable charging times (for coordinated charging mode). The outputs of the analysis are changes in air quality, EV energy consumption, EV peak demand, and EV hourly consumption profile—before and after project implementation." For a detailed description of the tool methodology, including all the publicly available datasets used by the tool, see the [GridPIQ documentation](https://gridpiq.pnnl.gov/v2-beta/doc/).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Barriers and Opportunities for U.S. Hydropower Industry Engagement in Brazil and Argentina

This Barriers and Opportunities for U.S. Hydropower Industry Engagement in Brazil and Argentina project seeks to enhance the understanding of hydropower resources, existing installations, and opportunities for U.S. company involvement in hydropower development in Brazil and Argentina. It covers conventional hydropower, pumped storage hydropower, run-of-river-hydropower, in-conduit generation, hydropower upgrades to existing facilities, and similar systems. This work recognizes that each of these countries, and even regions within the countries, have varied topography, weather, existing grid infrastructure, social, economic, and other factors, leading to relatively unique opportunities and challenges for hydropower development.

13 HYDRO ENERGY↗

DOE OSTI2021

Electrification of Aircraft: Challenges, Barriers, and Potential Impacts

Small-scale aircraft are being electrified with small e-aircraft already certified to fly and with test flights underway for retrofits of existing aircraft focused on near-term deployment. Internationally, from 2018 to 2020, the number of electric aircraft projects underway increased 50%. These technologies are projected to increase operational efficiency and reduce emissions and noise from a growing aviation sector. This emerging interest in aviation electrification includes aircraft, energy supply equipment, and battery storage manufacturers. Federal agencies are currently funding a variety of efforts including technology research as well as forecasting demand and environmental impacts and state governments have also begun pushing for further investigation in this area. This paper provides an overview of the current state and potential future development of aviation electrification. To understand the types of research questions around current and future challenges for this emerging sector, we consider a case study of the potential deployment of small commercial electric aircraft for existing, under-served markets and consider operational requirements and technical challenges as well as the range of questions that will need to be addressed around charging infrastructure, grid integration, policy and regulatory requirements, and emissions impacts. This case study also outlines potential nearer-term solutions and research areas of interest that will need to be addressed in the longer term at much larger scales and highlights NREL capabilities for addressing these challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Homomorphic Encryption for Electrical Metering Aggregation: Protecting the Privacy of Building Tenants

Electrical meters are devices that measure consumer electricity usage. The data collected by these meters is necessary for utility billing and electrical grid management but can also be used to assess the environmental impact of buildings. Prior research has found that unprotected metering data could potentially be used to infer some information about the behaviors of building tenants by detecting changes in electricity usage. For example, a period of low electricity usage could suggest that the tenants are not in the building. As smart metering becomes more common, there is a growing need for data privacy protections for metering data that do not negatively impact the quality and availability of data used for energy management and billing applications. To identify potential solutions, we developed a Python-based data aggregation platform to analyze the potential efficacy of privacy-enhancing technologies for energy metering applications. This platform aggregates groups of metering sites into virtual buildings, which could potentially detach changes in electrical activity from individual tenants, making it more difficult to track the activity of a specific tenant. To further protect data during analysis, this project utilizes homomorphic encryption as part of its initial approach. Homomorphic encryption offers a means of protecting energy consumption data while permitting mathematical operations to be performed without the need to know the data contents. This allows for data to be processed into usable statistics without revealing energy consumption information. A series of homomorphic encryption libraries were evaluated to determine their applicability and limitations in the context of metering data. The use of these techniques may help to reassure consumers and encourage further adoption of smart grid infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Mini Guide: Community Engagement Approaches for Distribution System Resilience

The focus on electric grid resilience has gained significant momentum in recent years. The imperatives behind this focus are multifaceted, including the escalating threats of climate change, extreme weather events, and cyber and physical attacks. These challenges expose the vulnerabilities of the country’s aging grid infrastructure and underscore the important

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Principles and Options for Designing Battery Energy Storage Zoning Ordinances

Deployment of battery energy storage (BESS) systems, both standalone and as part of hybrid systems paired with generation, has rapidly increased in the United States in recent years as utilities and communities have deployed storage to improve electric grid reliability and act as a cost-effective alternative to larger grid infrastructure. The modular nature of BESS technologies means systems may be built near other existing land uses, creating the potential for conflicts with neighboring landowners that can be managed and mitigated through zoning and permitting requirements established by local jurisdictions. While many cities and counties have adopted ordinances specific to BESS into their local zoning codes, these ordinances vary significantly in their requirements and level of detail. Meanwhile, many other jurisdictions, including those home to proposed or existing BESS projects, lack any specific language related to BESS in their zoning codes. Local planning and zoning officials have limited capacity and may lack the familiarity with BESS technologies needed to develop ordinances or otherwise make reasonable zoning decisions that balance safety, community impacts, and other goals. The resulting uncertainty at the local zoning level has led developers to withdraw projects in some areas and has spurred moratoria or bans on energy storage projects in others. This report intends to provide practical resources for practitioners interested in reasonable and effective local regulation of battery energy storage. It does not present a model zoning ordinance, but rather is intended to complement model ordinances developed by others by providing additional context and analysis regarding the structure of energy storage zoning ordinances and the decision points for local officials. Zoning ordinances at the city, town, and county level across the U.S. were surveyed alongside two template model ordinances to identify common elements and options for regulating the zoning and siting of BESS. Common elements identified and analyzed include definitions and general requirements, including cutoffs or tiers used to apply regulations to different system sizes and the permitted zones where jurisdictions allow BESS to be sited; visual, noise, and aesthetic requirements, including property line setbacks, fencing and visual screening, noise, and lighting requirements; and safety and planning requirements, such as site plans, decommissioning plans or funds, and requirements for access by emergency services. The report also summarizes some of the more unique regulations, including those that place additional restrictions on BESS at the local level.

25 ENERGY STORAGE↗

DOE OSTI2026

Regulatory and Technical Challenges and Barriers to Adoption of Distributed Wind Energy in Agricultural Settings

Distributed wind (DW) energy development can benefit agricultural landowners through the possibility of improved resilience of electrical service from onsite generation and associated economic benefits. DW development currently faces technical and regulatory challenges related to interconnection of projects with distribution or transmission equipment on the main electrical grid. A review was conducted of barriers to adoption for agricultural DW projects and potential roles of various stakeholders in addressing them. One major barrier is that the unique characteristics of wind energy generation such as its variability and intermittency, may require upgrades for the whole power line, which can lead to prohibitive cost burdens on individual interconnection customers. Another barrier is compliance with federal, state, or utility-level regulations that require technology-agnostic, industry-standard equipment that is often not technically realistic for DW projects. DW developers, grid infrastructure owner-operators, regulators, and standards publishers can work together to address these challenges and facilitate DW development.

17 WIND ENERGY↗

DOE OSTI2026

Impacts of Regional Coordination on Transmission Needs for Power System Resource Adequacy [Slides]

Resource adequacy (RA) is an important component of bulk power system reliability that addresses whether there are sufficient resources available to meet electricity demand at all times. RA analysis is used to assess whether the planned power system will provide reliable electricity to consumers while accounting for equipment outages, weather variability, and load uncertainty. Coordination between regions can enable resource sharing to meet RA needs if sufficient transmission capacity exists. Transmission's role in RA coordination can be particularly pronounced for large power systems like the U.S. electricity grid which contains geographically diverse demand and weather-dependent resources. Depending on the level of coordination desired, existing inter-regional transmission capacity may not be sufficient. This study is designed to assess optimal pathways for the development of inter-regional transmission in the U.S. under varying levels of RA coordination. Results can inform long-term grid infrastructure planning and provide insights into potential benefits of greater coordination for generation and transmission planning between regional U.S. power systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Pilot Development Progress to Demonstrate Electric Thermal Energy Storage (ETES) Using Low-Cost Particles

The rapid growth of future energy demand increases the need to economically store electrical energy over durations up to several days in long-duration energy storage (LDES) applications. LDES can reduce the grid stress for improving the resilience of the grid. The integration of renewable power and storage has several significant and positive impacts including expanding the renewable energy portion of electricity generation, meeting peak-load demand, and coordinating supply and demand. Several energy storage approaches including mechanical, chemical and electrochemical methods are currently deployed or under development. However, LDES requirements pose unique challenges for scalability, energy capacity, and cost. Thermal energy storage (TES) has the ability to store a large capacity of energy with improved site flexibility compared to incumbent LDES technologies (i.e., pumped hydro) and has attracted significant interest. Our development in TES technology using solid particles as the storage medium has shown feasibility and potential in serving LDES purposes. Particle TES has various advantages over commercialized molten nitrate salt TES and has emerged as a promising storage technology originating from Generation 3 concentrating solar power (CSP) development. Stable, inexpensive silica sand produced in the Midwestern U.S. was identified as a suitable storage medium and has been validated through lab heating and cycling tests. We have successfully developed particle TES for bidirectional electric energy storage to broaden the applications for TES beyond CSP technologies and into standalone electric-thermal energy storage (ETES) systems. Key component designs and performance were verified with prototype testing and model simulations. The ETES system uses high-temperature, low-cost particle thermal energy storage coupled with a unique pressurized fluidized bed heat exchanger that supports a high-efficiency, air-Brayton combined power cycle. At times of low energy demand or high renewable production, an electric particle heater heats large quantities of solid particles to high temperatures in excess of 1100 degrees C. The particles are stored in internally insulated silos. At times of high electricity demand, the hot particles are gravity-fed through the heat exchanger where stored thermal energy is transferred to the power cycle working fluid to drive the gas turbine power generation system, thereby converting the thermal energy back into electricity for the grid. The cold particles leaving the heat exchanger are returned to the storage silos by a well-insulated particle conveyor. This ETES technology using particle TES can be sited anywhere and is predicted to be economic and efficient. Moreover, this ETES technology could be co-located with a retired coal or gas plant to leverage existing power generation and grid infrastructure. Our work has focused on the development of each of the key components (e.g., the electric particle heater, particle storage silos, and the particle-to-gas pressurized fluidized bed heat exchanger) through conceptual designs, prototype testing, and computational modeling. These developments demonstrate technological feasibility and are an important step towards the next phase of development of this promising LDES technology. This presentation will show the progress in developing a pilot demonstration of the ETES system on a commercially relevant scale.

25 ENERGY STORAGE↗