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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Addressing the Split Incentive Challenge for Enhanced Solar Adoption in Multifamily Rental Properties [Abstract]

The split incentive problem is particularly pronounced in rental markets, where landlords prioritize investments that directly increase property value or rental income. Since energy savings from solar photovoltaic (PV) systems primarily benefit tenants, landlords may perceive little return on investment unless mechanisms exist to recapture some of the financial gains. The primary objective of this project is to develop a publicly available, web-based tool to analyze the U.S. Department of Energy’s ResStock database, which models the U.S. residential building stock. The tool allows users to filter buildings by location, type, HVAC system, square footage, and other characteristics, and outputs typical electric load profiles. By leveraging location-specific electric load data, Fram Energy aims to advance business strategies that address the split incentive barrier and promote the adoption of solar PV installations in rental properties. In addition, a machine learning model will be developed to weigh the marginal contribution of building features across the dataset in predicting electricity demand, supporting guided decision making in forecasting electric load profiles. Lastly, based on each building’s location, load profile, and utility’s electricity rate, an optimized solar photovoltaic array and battery energy storage system will be sized to provide energy arbitrage opportunities.

14 SOLAR ENERGY↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Detailed Laboratory Evaluation of Electric Demand Load Shifting Potential of Controlled Heat Pump Water Heaters

The demand profile management of electric end uses is vital research for utilities and policymakers planning greenhouse gas emission reductions. In this study, detailed laboratory research was conducted on the load shifting potential of 4 grid-connected HPWHs and one electric resistance water heaters (ERWH). The testing applied different CTA-2045 shed and critical peak command designs under three water draw profiles. Highly-controlled laboratory experiments were conducted in Florida. One of the four HPWHs was a prototype incorporating the new CTA-2045-B protocol feature allowing ‘advanced’ load-up above tank setpoint. A three-hour morning (6 – 9 AM) and four-hour evening curtailment (4 – 8 PM) were defined as the shed or critical peak periods reflecting high-value control periods for utility coincident load for system-wide electric demand reductions. Tests were performed under baseline conditions (no load shift) and under varied load-shifting schemes, including load up and advanced load up, ahead of shed and critical peak commands. Data were collected from December 2020 – February 2022 in the laboratory and compared with field experiments in Florida and the Pacific Northwest. Grid-connected HPWHs were found to reduce peak demand by up to 0.47 kW compared to uncontrolled HPWH units, depending on time of day, control scheme, draw profile, and temperature cluster. The load up strategy demonstrated the ability of all units to utilize heat pump mode for extended periods ahead of peak events. Demand reductions for the HPWHs were much larger when compared with the ERWH— up to 1.64 kW with large hot water draws in winter.

Fenaughty, Karen↗
DOE OSTI2024

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.

Computer Science↗
DOE OSTI2024

Nonlinear behavior in high-frequency aggregate control of thermostatically controlled loads

Coordinated control of electric loads can provide valuable grid services, such as frequency regulation. However, due to the nonlinear characteristics of such load ensembles, it is important to systematically analyze their behavior and establish a thorough understanding of undesirable phenomena that can potentially arise. In this paper, we analyze the frequency response of an aggregate control scheme, with the goal of exploring controller performance limits. We show that rapid switching commands can induce oscillations in the power output due to the inherent lockout mechanism of the underlying devices. Here, we demonstrate that highly detailed aggregate models are required to capture such phenomena. Such models enable deeper understanding of the control boundaries and therefore play an important role in avoiding the introduction of undesirable effects on the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Synergies Between Building-Sited Batteries and Thermal Energy Storage

As renewable penetration increases, there is a greater need for energy storage systems located at buildings. This storage can include batteries, which directly shift the metered load, or thermal energy storage, which shifts thermal-driven electric loads like air conditioning. This presentation covers modeling results of the potential demand reduction and annualized cost savings for different combinations of thermal and battery energy storage sizes. It also shows how battery storage can expand the usefulness of thermal energy storage for electric load shaving, and how thermal storage can lower the cost of the overall storage system and extend battery lifetime by reducing cycling.

batteries↗
DOE OSTI2024

Impact of Geothermal District Heating System on Flexibility of Microgrid in Tuttle, Oklahoma: Preprint

Flexibility is the capability of the power grid to maintain a balance between electricity generation and variable demand. This study presents preliminary results evaluating the impact of geothermal district heating systems on the flexibility of a conceptual microgrid in Tuttle, Oklahoma. Heating demand profiles were modeled using EnergyPlus for the district that includes two schools and 250 single-family houses. Then, geothermal energy production was modeled using GEOPHIRES to estimate how much heating demand in the district can be supplied by five different geothermal system scenarios. The results indicated that geothermal energy production varied depending on the resource temperature at different depths, system configurations, and flow rates. For the grid flexibility analysis, electricity consumptions in the five geothermal systems were estimated for pump operations to circulate water from the wells to radiators, while electricity consumption by air-source heat pump in the base case was estimated to supply the same heating load. Electricity consumption in the geothermal systems was significantly lower than those in base cases. The electricity saved by the geothermal system was then incorporated into the microgrid electrical load profiles where variable renewable electricity generation is significantly high. The results visually showed that geothermal district heating system can improve grid flexibility as a baseload during the winter season. The results also highlighted potential opportunities to save energy costs that will be further analyzed in future study.

Cambium↗
DOE OSTI2025

Impact of Geothermal District Heating System on Flexibility of Microgrid in Tuttle, Oklahoma

Flexibility is the capability of the power grid to maintain a balance between electricity generation and variable demand. This study presents preliminary results evaluating the impact of geothermal district heating systems on the flexibility of microgrid in Tuttle, Oklahoma. Heating demand profiles were modeled using EnergyPlus for the district that includes two schools and 250 single-family houses. Then, geothermal energy production was modeled using GEOPHIRES to estimate how much heating demand in the district can be supplied by five different geothermal system scenarios. The results indicated that geothermal energy production varied depending on the resource temperature at different depths, system configurations, and flow rates. For the grid flexibility analysis, electricity consumptions in the five geothermal systems were estimated for pump operations to circulate water from the wells to radiators, while electricity consumption by air-source heat pump in the base case was estimated to supply the same heating load. Electricity consumption in the geothermal systems was significantly lower than those in base cases. The electricity saved by the geothermal system was then incorporated into the microgrid electrical load profiles where variable renewable electricity generation is significantly high. The results visually showed that geothermal district heating system can improve grid flexibility as a baseload during the winter season. The results also highlighted potential opportunities to save energy costs that will be further analyzed in future study.

Cambium↗
DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗
DOE OSTI2021

Charting a Path for Research and Development of Reliability and Resilience in South Asia's Power Sector

The power sector in South Asia faces several trends with the potential to impact its reliability and resilience. Rapidly increasing demand, coupled with an increasing reliance on variable renewable resources and the circular linkages with climate change points to an increasing need to understand the extent of climate impacts on both the electricity load and the electricity generation. These larger shifts are also coupled with opportunities near the grid edge that could have a large impact on system planning and operations, such as electrification of the transport sector, increased reliance on buildings to serve a broader set of loads and be flexible resources for utilities, more efficient use of industrial and agricultural loads, and growth in distributed energy resources such as rooftop solar and batteries. It is critical that as this transformation takes place that expectations for reliability and resilience of the grid continue to increase in the region. The South Asia Group for Energy (SAGE), composed of USAID, the US Department of Energy, and three national laboratories, has been tasked with providing an overview of the research and development resources necessary to understand the upcoming challenges for the power system as it pertains to reliability and resilience. This discussion paper identifies some of the key trends and connections that are important for power sector reliability and resilience and provides a starting point for eliciting feedback from power sector stakeholders about their experiences and needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

DERMS-RT (Distributed Energy Resource Management Solution using Real-Time Optimization) [SWR-20-46]

DERMS-RT provides a distributed energy resource management solution using real-time optimization to control different types of distributed energy resources (DERs) — such as rooftop PVs, battery energy storage systems, HVAC loads, electric water heater loads, and EV charging loads, and use these DERs to provide distribution voltage regulation and peak demand management services. The control approach implemented by DERM-RT is developed based on the real-time optimal power flow and distributed control previously developed by NREL researchers. The software codes are designed to provide modular DER management solution that can be easily applied to control heterogeneous DERs in distribution grids in coordination with centralized utility management systems. Also, DERMS-RT provides application programming interface (API) to extract useful grid model information from an open-source power system simulation software, use the information to solve the optimization problem, and apply the optimal set-points to the controlled DERs. In this way, DERMS-RT provides a plug-and-play function for implementing the real-time DER control on different utility system models, and it can be used as a simulation testbed to demonstrate the impact of real-time DER optimization on improving grid operations.

Ding, Fei↗
DOE OSTI2025

Distribution Substation Planning Toolkit (dsp-toolkit) v1.0

The Distribution Substation Planning Toolkit (DSP Toolkit) is a software suite designed to streamline the planning and optimization of distribution substations. This toolkit offers a comprehensive set of tools and APIs for data curation, short-term electric load forecasting, and weather-sensitive load adjustment, making it an essential resource for utility companies, engineers, and researchers. Features • Data Preprocessing and Curation: Efficiently manage and preprocess large datasets to ensure high-quality input for analysis. • Short-Term Load Forecasting: Utilize data-driven models to predict short-term electric loads accurately. • Weather-Sensitive Modeling: Automatically adjust load forecasts based on weather data to predict future peak demands more precisely. Uses The DSP Toolkit is ideal for planning and optimizing distribution substations, providing a user-friendly interface and comprehensive documentation. It is suitable for both novice and experienced users, facilitating efficient and accurate planning processes. Advantages • Efficiency: Automates complex planning tasks, reducing manual effort and minimizing errors. • Scalability: Handles large datasets and complex models, making it suitable for large-scale projects. • Community and Support: Open-source with active community contributions, ensuring continuous improvement and support. • Extensibility: Easily extendable with custom modules and plugins, allowing users to tailor the toolkit to their specific needs. The DSP Toolkit stands out by offering a robust, flexible, and user-friendly solution for distribution substation planning. Public Abstract

Li, Han [Lawrence Berkeley National Laboratory (LB↗
DOE OSTI2023

Study on Electrostatic Separation of Quinoline Insolubles from Coal Tar Pitch

The feasibility of electrical separation in the removal of quinoline insoluble (QI) particles from coal tar pitch (CTP) was experimentally investigated. QI particle involvement prohibits the effective fabrication of high-quality value-added products, such as carbon fibers, from CTP. A substantial and sustainable CTP market exists around the world; therefore, a strong incentive exists to develop a viable technical approach to effectively and economically remove QI particles from CTP. The electrical separation method shows promise to achieve this technical goal (Cao et al., 2012). Even with the given setup (wire- cylinder adapted), critical issues remain to be addressed for this method to be applicable to the CTP: (1) identifying the wash oil used in the original QI separation, (2) understanding the mechanism of QI separation, (3) characterizing the deposit, and (4) identifying the QI. This study integrated a set of experimental and analysis techniques into the electrical separation tests to address the aforementioned issues. Key findings are as follows: 1. Electric current responses: • The electric current level of the CTP–wash oil solution reported by Cao et al. (2012) can be attained by using a mixture of 25% quinoline and 75% toluene for the CTPs examined in this study under the same electrical load condition. • The electric current tends to decrease during the test period because of the decreasing number of charged particles. • The reversed field corresponded to the configuration of electrostatic precipitation for positive corona discharge. The high electric field can result in dielectric breakdown and lead to an abrupt current surge. 2 Deposit response and solvent candidates: • Deposit of particles in CTP mixture can be effectively implemented in a wire-cylinder configuration as proposed and examined in this study. • Deposit depends on the solvents. Among the solvents tested, two- and three-part solvents that included quinoline and ethanol (i.e., 25% quinoline and 75% toluene; 50% quinoline and 50% toluene; and 33% wash oil, 33% BTX, and 34% ethanol) produced the highest deposit weight. • The deposit process examined in this study is derived from the charged particles. An appreciable relation exists between deposit weight and electric charge. 3. QI removal efficiency (RE): • The RE of the electrostatic separation can reach as high as 76.2% for Carbores, and 61.6% for Koppers. • The RE can be further enhanced if the field level increases from 0.16 kV/mm that was used in the current study to 0.23 kV/mm, according to the relation established between the RE and the electric field. 4. Testing of the modeling system: • The motion of particles is originally driven by the charge-based electric force in the cases tested. The solid particle separation mechanism is similar to that of QI deposition in CTP. • The mechanical movement of solid particles can be strongly affected by the gravitational and viscous forces in the electric field. 5. EDS analysis and chemical compositions: • The main chemical composition of deposit QI matches that of as-prepared CTP QI. The deposit QI is derived from the same group of the CTP QI. • In addition to carbon, the QI contains oxygen, sodium, aluminum, silicon, iron, and sulfur. The work for the near future is also discussed.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Automatic Receptacle Controls: Evaluating the clarity of receptacle markings

Plug loads and the electric loads of devices plugged into receptacles in commercial buildings play a significant and growing role in commercial buildings. Because plug loads are portable and are often placed in the building by the occupants, they are a challenging load to manage. This report summarizes an analysis of commercially available automatic receptacle controls (ARCs). As a part of this study, generic versions of ARCs were presented to a general user audience via an online survey, wherein survey respondents indicated which markings more clearly indicated that the receptacle was controlled. A total of 210 responses were collected and included in the analysis presented in this report. The results showed that receptacles with high-contrast markings were consistently clearer and more evident to the users than low-contrast markings. The survey responses also revealed that receptacles with a border drawn around each controlled receptacle were clearer and distinct to the users compared to other markings. These results led to the conclusion that for energy saving technology to be effective, consistent and easy to understand markings are necessary.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Powered by dsgrid [Slides]

NREL's demand-side grid (dsgrid) toolkit harnesses decades of sector-specific energy modeling expertise to understand current and future U.S. electricity load for power systems analyses. The primary purpose of dsgrid is to create comprehensive electricity load data sets at high temporal, geographic, sectoral, and end-use resolution. These data sets enable detailed analyses of current patterns and future projections of end-use loads. This presentation will include NREL power grid researcher Elaine Hale.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

CalderaCast User Manual Version 1.0

CalderaCast is a user-friendly web-based tool for electrical-load forecast, providing stakeholders with a fully customizable decision-support framework that estimates the likely power draw from a possible future electric-vehicle (EV) charging station at a given location on a given day along an alternative fuel corridor (AFC). These EV charging profiles are accurately modeled in CalderaCast using the Caldera software framework developed by Idaho National Laboratories (INL), reflecting the realistic charging levels observed in actual charge events. This tool was developed as part of the National Electric Vehicle Infrastructure (NEVI) program, which is quickly generating substantial interest from would-be charging station operators (CSO), large and small electric utilities, and state transportation planners, some of whom had not seriously considered EV charging previously. All these entities—with or without background in EV infrastructure—must estimate the electricity load that a proposed charging station will generate. This load forecast is critically important for a utility to properly assess the capacity of their distribution network to support the proposed station or properly size grid upgrades for potential load growth due to future EV adoption, vehicle technology improvements, or station growth. This document describes each aspect of the CalderaCast tool and provides guidance to users who are interested in utilizing the tool for their work.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Field Evaluation of the High Efficiency Dehumidification System (HEDS) at the Timken Museum of Art - Measurement and Verification (M&V) Results from Summer, Winter and Spring Evaluation Periods

The High Efficiency Dehumidification System (HEDS) technology from Conservant Systems Inc., installed at the Timken Museum of Art in San Diego, California, was evaluated to determine its performance relative to appropriate baseline operation. Data was collected for measurement and verification for several weeks during three evaluation periods: Summer (Aug-Sep 2023), Winter (Dec 2023-Feb 2024) and Spring (May-Jun 2024). The evaluation included operating the heating, ventilation and air conditioning (HVAC) system in both constant air volume (CAV) and variable air volume (VAV) modes, with and without the HEDS energy recovery and HVAC system optimization technology enabled. The electricity consumption of the chiller and the gas-supplied reheat energy were measured to characterize savings achieved by the HEDS operation. Based on these measurements, the HEDS was responsible for chiller electrical load savings during the summer evaluation period of 39% and 42% for the CAV and VAV operating modes, respectively; 97% and 100% chiller electrical load reductions were observed during the winter evaluation for the CAV and VAV operating modes, respectively; and the corresponding reductions during the spring evaluation were 42% and 52% for CAV and VAV operation, respectively. The measured reheat energy reductions, which are typically provided by natural gas, due to the HEDS during summer were 64% and 97% in CAV and VAV operating modes, respectively, while the corresponding values were 99% and 78% during the winter evaluation, and 59% and 56% during the spring evaluation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗