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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Brick Schema Standardized Plug Load Control Strategies for Load Reduction: Preprint

Plug loads comprise a significant percentage of commercial building energy consumption. Applying intelligent controls to turn off plug loads when unused can provide dynamic load reduction and flexibility, which are key traits of grid-interactive efficient buildings. This capability is important for equitable decarbonization as it can enable disadvantaged communities to electrify buildings without costly upgrades to electrical infrastructure. In this work, we present the effectiveness of various control strategies along with the operational lessons that informed their design. During a three-year period, we operated over 600 smart outlets in 12 university office buildings. The attached plug loads consisted primarily of printers, TVs, water dispensers, and copiers. After recording baseline power measurements for one year, we designed plug load control (PLC) strategies for each plug load type, use, and for different risk tolerance levels because PLC can potentially be disruptive to daily work. We used the Brick Schema to facilitate the management of plug load locations and other metadata. For advanced controls, we integrated the smart plugs with heating, ventilation, and air conditioning (HVAC) systems through the campus building automation system. We found static schedules to be the least disruptive and most predictable for occupants, resulting in 38% and 66% energy savings in two studies. For printers, print server-triggered PLC produced 86% savings, the highest of all strategies with minimal occupant impact. Scheduling of water dispensers and digital signage TVs produced 49% and 70% savings respectively with opportunities to improve performance with the use of HVAC occupancy data.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2024

Experimentation in Exploring Photovoltaic Inverter Dynamics Under Different Irradiance Levels Through a Data-Driven Approach

As conventional direct connections of synchronous generators are being phased out, inverter-based resources (IBRs) with grid support functions are increasingly being integrated into power systems. This transition requires the development of accurate dynamic models for IBRs to predict how power systems will adapt to varying levels of IBRs penetration, establish grid code requirements, and ensure compliance. Here, this study introduces an active probing signal-based data-driven modeling technique to accurately derive the dynamics model of a smart photovoltaic inverter operating in Volt-Watt and Freq-Watt modes, in compliance with the IEEE 1547–2018 standard. The paper focuses on investigating how the dynamics of the PV inverter model respond to fluctuations in solar irradiance, utilizing real-time digital simulator experimentation. The experimental analysis demonstrates that the amplitude of dynamics fluctuates with changes in irradiance across both operational modes and confirms the active power’s dependence on irradiance levels. Furthermore, the nature of inverter dynamics varies distinctly between the different modes of activation. Critically, our findings indicate that dynamic models require DC-gain adjustments to accommodate contrasting irradiance levels, highlighting a negative gradient linear relationship between the DC-gain of each model and the irradiance.

14 SOLAR ENERGY↗
DOE OSTI2025

DistOPF: Advanced Solutions for Distribution Optimal Power Flow Analysis - DistOPF v0.2 Documentation

To achieve an affordable and reliable energy system, research on power distribution system is often focused on integration of distributed generators, energy storage solution, EV charging, smart meters, and other advanced assets that may benefit from or require more advanced control and optimization techniques. Despite this focus on advanced distribution system topics, early researchers and grid scientists often start from scratch when developing optimization programs for power distribution systems. This report introduces DistOPF, a Python package that consolidates years of research into a versatile and modular tool. DistOPF provides researchers with essential capabilities to solve distribution system Optimal Power Flow (OPF) problems using standard network models. Additionally, it offers a platform to benchmark both new and existing algorithms against established test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Survey and Gap Prioritization of U.S. Electric Vehicle Charge Management Deployments

The goal of this study was to survey and characterize the scope of current technical and programmatic knowledge pertaining to EV charge management technologies and practices in the US and relevant international jurisdictions. This characterization of existing field demonstrations and knowledge derived were used to determine gaps in the SCM demonstration landscape. Addressing these gaps through research and demonstration could increase confidence in the U.S. that load management and EV charge control could achieve overarching societal benefits. A survey of charge management deployments and input from stakeholders was completed to determine the state-of-the-art of smart charge management (SCM) where SCM is defined as controlling the amount of power exchanged between chargers and EVs to meet customers' charging needs while also responding to external power demand or pricing signals to provide load management, resilience, or other benefits to the customer and electric grid. The survey was the basis of the gap analysis in this report and determines which areas are well understood, with high confidence, and which areas need further investigation. Existing examples of EV charge management are characterized here to determine aspects that are ready for widespread deployment and have been demonstrated in the field. These include demonstration studies, pilots, programs, and EV-specific tariffs. In all, 110 examples of charge management were characterized. The data sources were public literature and utility filings as well as targeted interviews. In addition, 43 interviews with stakeholders were conducted with a consistent set of questions used in each interview. This study prioritized gaps in demonstrated SCM capabilities based on 1) Urgency of the particular use-case to offset traditional grid assets, 2) Impact, extensibility, and scaling of results across the entire spectrum of 3000+ utility service territories including projected technical and market potential for a given grid service, and 3) Value of federal funding in addressing the gap, including potential to leverage and/or add scope to existing field demonstrations funded by other non-federal funding mechanisms.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

V1G Frequency Regulation: Algorithm Development, Validation & Analysis at Scale

Researchers at Argonne National Laboratory developed and validated a high-fidelity digital twin of a smart charging (V1G) ecosystem to model the participation of up to 1,000 unique electric vehicles (EVs) in the PJM frequency regulation market. Utilizing a discrete-event framework, the simulation models complex interactions, from dynamic grid signals (updated every 2 seconds) to individual EV charging dynamics. The simulation incorporates multiple EV models created from real-world lab test data. Researchers tested multiple control algorithms to balance the dual objectives of maximizing aggregator’s revenue and driver charging needs. Results demonstrate that aggregated EVs function as a controllable, highly effective grid resource, achieving high PJM Performance Scores (80–90%). Additionally, an optimized, market-aware bidding strategy was identified as key to profitability. The platform was shown to provide drivers with an average charging discount of nearly 50%. The algorithm was further validated in the lab using production EVs and charging stations to compare simulation results with real-world performance.

Manne, Nithin↗
DOE OSTI2025

Final Technical Report of a Feasibility Investigation of a Hydropower Flexibility Upgrade Kit

The project studied the feasibility of a concept for a flexibility upgrade kit called h-Dynamyk to improve the flexibility of U.S. hydropower plants and assessed its potential impact on the U.S. hydropower industry. This concept addresses the growing need for grid flexibility amid rising renewable energy adoption, such as wind and solar, which often introduce intermittency. The upgrade kit is intended to address challenges posed by aging turbine designs in meeting modern grid demands for flexibility services like frequency response, voltage control, spinning reserve, and black-start capabilities. Key features of the h-Dynamyk kit include siphon turbine assemblies, medium-voltage variable frequency drives (VFDs), and smart controls integrated with plant systems to optimize operations and extend equipment life. This nine-month discovery project successfully achieved its objectives by defining a baseline design and performance metrics for the h-Dynamyk flexibility upgrade kit, analyzing this baseline, and incorporating lessons learned from this analysis into a modified set of metrics and recommendations for future design and testing, which were further guided by field site studies.

13 HYDRO ENERGY↗
DOE OSTI2025

Impacts and emerging research opportunities in Vehicle-Grid Integration for transportation: A review

This review provides a comprehensive examination of Vehicle-Grid Integration (VGI) technologies and their impacts on transportation systems, with a particular emphasis on the transportation-energy nexus. It systematically explores how VGI affects key transportation applications such as charging infrastructure planning, electric vehicle (EV) routing, smart charging coordination, shared mobility, and dynamic pricing. By synthesizing recent literature from both transportation and energy systems perspectives, this study highlights how advanced methodologies, such as reinforcement learning, game theory, and optimization techniques, are used to model the complex interactions between EVs, mobility patterns, and distributed energy systems. Furthermore, the review also identifies critical challenges, including behavioral factors, data limitations, and system scalability. Drawing on these insights, the paper outlines emerging research opportunities to support the design of integrated, resilient, and user-centric VGI solutions that advance sustainable mobility and energy system efficiency.

Charging coordination↗
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Managing Increased Electric Vehicle Shares on Decarbonized Bulk Power Systems

Transportation electrification and power sector decarbonization - the convergence of these two trends present a complex planning problem requiring realistic, region-specific modeling and analysis to ensure that both can happen rapidly and cost effectively. This project, funded through the U.S. Department of Energy's Vehicle Technologies Office, models the evolution of the U.S. bulk power system (through 2050) in response to large-scale EV charging across all on-road vehicle segments. We will assess the opportunity and value of demand-side flexibility (i.e., "smart" charging) for reducing energy costs, increasing renewable generation shares, and managing future EV loads on the bulk power system. Overall, this study aims to provide an improved understanding of least-cost solutions for managing EV load growth on the grid and will make high-resolution EV load data sets publicly available for further analysis.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2026

Analyzing SCM Grid Benefits from Electric Transportation [Slides]

Increasing adoption of EVs and expanding unmanaged charging loads could increase the cost of transportation energy due to increasing load variability and shrinking infrastructure capacity. The actual cost of transportation energy, such as charging an EV, depends on several factors including energy costs, charging infrastructure costs, and applicable grid upgrades. Based on studies from past DOE projects; RECHARGE, DirectXFC, FUSE and 21st Century Truck Partnership (21CTP) the EV-CENTS project will develop a transportation energy cost metric to better quantify these factors and provide a framework for assessing the value potential of new technology solutions, such as smart charge management (SCM), which could reduce these costs for all stakeholders. The initial assessment will focus on the cost of charging, which will vary across vehicle classes such as light-duty vehicles (LDV) or medium and heavy-duty vehicles (MHDV), as well as across different vocations resulting in many different use cases for this metric. Cost of charging results will be developed for each use case in both uncontrolled and controlled scenarios to understand the value potential of different SCM objective functions and their ability to optimize the cost of energy and delay or eliminate the need for electrical upgrades.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Smart Meter Data: A Gateway for Reducing Solar Soft Costs with Model-Free Hosting Capacity Maps

Public-facing solar hosting capacity (HC) maps, which show the maximum amount of solar energy that can be installed at a location without adverse effects, have proven to be a key driver of solar soft cost reductions through a variety of pathways (e.g., streamlining interconnection, siting, and customer acquisition processes). However, current methods for generating HC maps require detailed grid models and time-consuming simulations that limit both their accuracy and scalability—today, only a handful out of almost 2,000 utilities provide these maps. This project developed and validated data-driven algorithms for calculating solar HC using data from AMI without the need of detailed grid models or simulations. The algorithms were validated on utility datasets and incorporated as an application into NRECA’s Open Modeling Framework (OMF.coop) for the over 260 coops and vendors throughout the US to use. The OMF is free and open-source for everyone.

14 SOLAR ENERGY↗
DOE OSTI2024

Slide Decks for EV@S Stakeholder Meeting at INL September 2024

These slides were used at the Electric Vehicles at Scale National Lab Consortium, Stakeholder Meeting hosted at INL in September 2024. The slides are now requested to be made available publicly for reference. All slides review projects that are already in the public domain and their information is intended for public release. They are not from sensitive topics or areas with any restrictions. A few projects are overviewed in detail and some slides were just an aid to the breakout sessions that were conducted with stakeholders and lab staff.

25 ENERGY STORAGE↗
DOE OSTI2025

Virtual Power Plants and Distributed Energy Resource Management Systems

Virtual Power Plants (VPPs) are aggregations of DERs that can balance electrical loads and provide utility-scale and utility-grade grid services like a traditional power plant. This presentation covers VPP definition, State-of-the-Art, Grid Architectures, Example VPP studies, VPP Standards, and VPP Roadmap.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

GreenThrift: Optimizing Carbon and Cost for Flexible Residential Loads

Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This paper highlights the potential conflict between electricity prices and carbon emissions and the resulting trade-offs in carbon-aware and cost-aware load scheduling. To address this trade-off, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources

Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Statistical Estimation of EV Driver Charging Behavior and Influential Factors

INL received data collected via telematics from battery electric vehicles (BEVs), and these vehicles were owned by retail customers who had entered into a telematics user agreement. The goal of analyzing these data was to develop mathematical models to characterize how different sets of BEV drivers use charging infrastructure at home and away from home (i.e., public charging) and quantify how various factors influence BEV drivers’ decision to charge and use available infrastructure. The data used in this analysis are unique because they provide real world BEV driving and charging behavior at the individual driving and parking event level. In this study we seek to leverage this data to quantify BEV charging and driving metrics to help inform models that predict quantities like the specific times when loads are imposed on the electrical grid due to BEV charging. Most models that have been developed to predict electrical grid load due to BEV charging, use simulations of BEV driving events and rely on assumptions such as every vehicle charges every night. Using a statistical modelling framework, we seek to investigate BEV charging behavior and quantitatively assess these common assumptions of BEV charging behavior.

33 - ADVANCED PROPULSION SYSTEMS↗