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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 91 records · Page 5

Electric Load Planning Tool (ELPT) v0.9

The Electric Load Planning Tool (ELPT) helps facilities understand the economic and environmental impacts of their electricity consumption. Using a user-provided Excel input, ELPT analyzes electricity use, costs, and grid CO2e emissions to identify savings opportunities through load management strategies such as load shifting, shedding, and planning. It accounts for Time-of-Use (TOU) tariffs and hourly emissions factors, varying by location and time of day. Users input details about their facility's load profile, location, year of analysis, and electricity billing tariff to receive customized insights. The tool provides visual representations of cost and GHG impacts, helping users understand the benefits of adjusting electricity usage to align with periods of cheaper and cleaner electricity, thereby achieving cost savings and reducing Scope 2 CO2e emissions

Karki, Unique [Lawrence Berkeley National Laborato↗

Assessment of Light-Duty Plug-in Electric Vehicles in the United States (2010 - 2020)

This report examines properties of plug-in electric vehicles (PEVs) sold in the United States from 2010 to 2020, exploring vehicle sales, miles driven, electricity consumption, petroleum reduction, vehicle manufacturing, and battery production, among other factors. Over 1.7 million PEVs have been sold, driving 52 billion miles on electricity since 2010, thereby reducing national gasoline consumption by 0.42% in 2020 and 1.9 billion gallons cumulatively through 2020. In 2020, PEVs used 4.4 terawatt-hours of electricity to drive 13.7 billion miles, offsetting 500 million gallons of gasoline. Since 2010, 68% of PEVs sold in the United States have been assembled domestically, and 77 gigawatt-hours of lithium-ion batteries have been installed in vehicles to date.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE EV Data Collection - Facility Data

Facility data includes information on electricity consumption by larger-scale infrastructure, including buildings, solar arrays, and energy storage systems. Parameter definitions can be found in the data dictionary. If a connection between specific vehicle information and facility data exists, it will be available in the vehicle attributes table. Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Assessment of Light-Duty Plug-in Electric Vehicles in the United States, 2010 – 2019

This report examines properties of plug-in electric vehicles (PEVs) sold in the United States from 2010 to 2019, exploring vehicle sales, miles driven, electricity consumption, petroleum reduction, vehicle manufacturing, and battery production, among other factors. Over 1.4 million PEVs have been sold, driving over 37 billion miles on electricity since 2010, thereby reducing national gasoline consumption by 0.34% in 2019 and 1.4 billion gallons cumulatively through 2019. In 2019, PEVs used 4.1 terawatt-hours of electricity to drive 12.7 billion miles, offsetting 470 million gallons of gasoline. Since 2010, 69% all PEVs have been assembled in the United States, and over 60 gigawatt-hours of lithium-ion batteries have been installed in vehicles to date.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Assessment of Light-Duty Plug-in Electric Vehicles in the United States, 2010 – 2019

This report examines properties of plug-in electric vehicles (PEVs) sold in the United States from 2010 to 2019, exploring vehicle sales, miles driven, electricity consumption, petroleum reduction, vehicle manufacturing, and battery production, among other factors. Over 1.4 million PEVs have been sold, driving over 37 billion miles on electricity since 2010, thereby reducing national gasoline consumption by 0.34% in 2019 and 1.4 billion gallons cumulatively through 2019. In 2019, PEVs used 4.1 terawatt-hours of electricity to drive 12.7 billion miles, offsetting 470 million gallons of gasoline. Since 2010, 69% all PEVs have been assembled in the United States, and over 60 gigawatt-hours of lithium-ion batteries have been installed in vehicles to date.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Understanding the Behavioral Aspects of Rate Design

Consumer demand for electricity has often been thought of as homogenous and inelastic. However, consumers may have preferences for their electricity consumption that they have been unable to realize due to the structures of electricity rates, and historic methods of regulation. As ratemaking becomes more dynamic, consumers will have a newfound ability to realize these preferences. This paper conducts a review of this issue through the lenses of utility regulation, neoclassical economics, and behavioral economics. It surveys what aspects of consumer preferences and behavior have been well established for the electricity sector and identifies gaps that could be filled by future research. This analysis has implications for stakeholders looking to understand how households will function in transactive and real time pricing environments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The Electrification Futures Study

This presentation explores widespread electrification in the future energy system of the United States, examining electric technology cost and adoption for end-uses across all economic sectors, electricity consumption growth and profiles, power system infrastructure development and operations, and the economic and environmental implications of electrification.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Geothermal-integrated thermally anisotropic building envelope for energy and peak-demand reduction

Buildings consume large amounts of energy for heating and cooling, while peak electricity demand places significant stress on the power grid. This paper presents a reduced-order co-simulation framework and load-oriented supervisory control strategy for a geothermal-integrated thermally anisotropic building envelope with a ground loop (TABE+GL). In TABE+GL, a hydronic loop embedded in the building envelope is directly coupled with a geothermal ground loop, allowing for bidirectional heat exchange between the envelope, the ground, and the indoor environment. A hybrid co-simulation framework was established by coupling a reduced-order resistor–capacitor (RC) thermal network model with EnergyPlus augmented with GHEDesigner modules. The RC model generated feasible heat flux options under three operating modes, and EnergyPlus predicted sensible loads, energy use, and pump energy demand. At each simulation step, a supervisory control algorithm selected the optimal loop configuration and duty factor that maximizes useful TABE geothermal utilization without exceeding the predicted sensible load, thereby avoiding overheating or cooling. Case studies were conducted for Los Angeles, California, Charleston, South Carolina, and Denver, Colorado. Results showed that the proposed framework reduced HVAC electricity consumption by 43%–67%, natural gas use for space heating by 11%–38%, and peak electricity demand by 43%–88%. These results highlight the potential of combining reduced-order envelope modeling, direct geothermal coupling, and load-oriented supervisory control to improve whole building energy performance and reduce peak demand across diverse weather conditions.

Howard, Daniel [Southern Adventist University]↗

Life cycle analysis of hydrogen production via methane pyrolysis using plasma arc

Steam methane reforming of natural gas is the primary method of producing hydrogen in the United States, accounting for 95% of all hydrogen produced there. Methane pyrolysis, an alternative production pathway that decomposes natural gas into solid carbon and hydrogen, both eliminates CO 2 emissions associated with methane reforming and allows for additional income from carbon black. A life-cycle inventory of this process has been developed using ASPEN Plus to model the methane pyrolysis (plasma arc) process. From well to gate, hydrogen production via methane pyrolysis produces 2.78 kg CO 2 e/kg H 2 of greenhouse gas emissions using mass allocation of emissions between hydrogen and carbon black coproducts. The well-to-gate emissions are mainly driven by electricity consumption (∼38 kW h/kg H 2 ), which accounts for 81% of the emissions; if renewable electricity is used, well-to-gate emissions can be reduced to −0.448 kg CO 2 e/kg H 2 .

08 HYDROGEN↗

High-Mileage Courier Fleet Vehicle On-road Logger Data

This dataset describes the performance and fuel efficiency of AVTA test vehicles operating in commercial courier fleets the Phoenix, AZ metro area between 2010 and 2016. Aftermarket data loggers were installed in two to four vehicles of each of 30+ distinct year/make/models (see reference ["INL Advanced Vehicle Testing Activity: On-road Logger and Laboratory Battery Pack Testing Vehicle List"](https://avt.inl.gov/sites/default/files/pdf/reports/DatasetVehicleList.pdf) for full list of vehicles). Loggers recorded vehicle operation as they were driven up to 160,000 miles in up to three years of fleet testing. Parameters were logged at 1-second intervals, including - vehicle speed, - engine and/or electric motor speed, - fuel and/or electricity consumption, and - ambient temperature. This dataset includes both raw second-by-second data and trip-level metrics. (This dataset will be shared by API; a small sample of the vehicle and logger data has been extracted and is available for download while the API is being developed.)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Hardware Specification and Reference Design for the Low-Cost, Interoperable, User-Centric, Supervisory Controller Kit for Small and Medium Size Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems to manage the various building systems they have. Many of these buildings are small (< 50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper control systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the RTU’s heating and cooling setpoints, schedules, setbacks, and optimal start times can result in 20 to 25 percent reduction in electricity consumption in small and medium commercial buildings (SMBs). In addition, improving the demand flexibility of these buildings will result in additional cost savings for the building owner. To address the needs of the SMBs, the Department of Energy’s Building Technologies Office jointly funded Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) to design, develop, test, and validate a low-cost, interoperable, user-centric, retrofit supervisory controller kit (SC-SMB) that can be used to continuously optimize energy consumption and deliver demand flexibility of SMBs, including all-electric buildings, and provide a means for maximizing decarbonization benefits. The team also includes industry partners Edo and Intellimation. Previously, the team drafted an SC-SMB system specification document (Goodman et al. 2023). This document describes a reference design of a low-cost supervisory control system for SMBs, including the various hardware components, software system, and an example implementation of the reference design. The final release of the reference design is planned for December 2025. Section 2.0 of the report documents the relevant building types that the SC-SMB system is suitable for. Section 3.0 documents the various hardware components, their function, cost, whether they are off-the-shelve, support standard communication, etc., and the software system. An example deployment in a 10,000-sf building with six rooftop units, a hot water heater, solar, and storage is described in Section 4.0. The planned next steps are described in Section 5.0, and references are listed in Section 6.0.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Modeling and Analysis of a Heat Pump Clothes Dryer with Thermal Energy Storage

Clothes drying is an energy-intensive process that causes significant electricity consumption and carbon emissions in the US. Approximately 83% of Households in the US own a tumble clothes dryer at home and 80% of dryers are electrical resistance dryers with low energy efficiency. Heat pump technology makes it possible for highly efficient and clean drying. Additionally, the ventless design of heat pump clothes dryers (HPCD) provides more installation flexibility. HPCD involves three primary mediums: wet clothes, a closed air loop, and a refrigerant circuit. The evaporator is used to dehumidify the wet air and the condenser is used to re-heat the dry air. One of the critical technological barriers to HPCD market penetration is its long drying time, primarily due to the relatively low discharging temperature and the slow response during the warm-up period. In this study, the thermal energy storage (TES) technology is adopted to address this challenge by providing pre-heating of air prior to the condenser to increase the operating temperature of the process air. The heat pump will charge the phase change material (PCM) in the TES device with heating energy during clothes washing and the PCM will discharge the stored heat to facilitate air heating during clothes drying. To analyze the optimal design and potential for energy saving and drying time reduction, a mathematical model of the HPCD system was developed. The HPCD is a highly dynamic system with a coupled heat and mass transfer and heat pumping cycle. This paper provides solutions to simulate the transient behavior of the system while maintaining low computational cost. The modeling result indicates reduced energy consumption and drying time by integrating TES with HPCD, as compared to electrical resistance dryers. The study can provide significant insights into improving building flexibility with TES and smart appliances.

Liu, Xiaoli↗

China's plug-in hybrid electric vehicle transition: An operational carbon perspective

Assessing the emissions of plug-in hybrid electric vehicle (PHEV) operations is crucial for accelerating the carbon–neutral transition in the passenger car sector. This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China’s top twenty selling PHEV models across different regions from 2020 to 2022. The results indicate that (1) the actual electricity intensity of the best-selling PHEV models (20.2–38.2 kWh/100 km) was 30–40 % higher than the New European Driving Cycle values, and the actual gasoline intensity (4.7–23.5 L/100 km) was 3–6 times greater than the New European Driving Cycle values. (2) The overall energy use of the best-selling models varied among different regions, and the energy use from 2020 to 2022 in Southern China was double that Northern China and the Yangtze River Middle Reach. (3) The top-selling models emitted 4.7 megatons of carbon dioxide nationwide from 2020 to 2022, with 1.9 megatons released by electricity consumption and 2.8 megatons released by gasoline combustion. Furthermore, targeted policy implications for expediting the carbon–neutral transition within the passenger car sector are proposed. In essence, this study explores and compares benchmark data at both the national and regional levels, along with performance metrics associated with PHEV operations. The main objective is to aid nationwide decarbonization efforts, focusing on carbon reduction and promoting the rapid transition of road transportation toward a net-zero carbon future.

33 ADVANCED PROPULSION SYSTEMS↗

A Decision Support Tool for Distributed Solar and Storage Investments: A Case Study in Austin, TX

This study builds a decision support tool to evaluate when it is a good economic decision (least cost with minimum discomfort) for the residential customer to invest in distributed energy resources (DERs) based on different electricity rate structures, DER ownership frameworks, and DER rebates offered by electric utilities. Here, the tool is demonstrated using empirical electricity consumption data from Pecan Street Inc. (a non-profit entity based on Austin, Texas), residential rates from Austin Energy (the municipal electric utility in Austin, Texas), DER ownership costs from various nationwide pilot programs, and incentives offered by electric utilities in the United States. Results show that for constant electricity rates, the overall expenditure is least when the customer owns solar panels without storage, while for time-varying pricing structures, the least expensive scenario is one where the customer does not own any DERs. As the capital costs for DERs decline, utilities incentivize customer ownership of DERs, and more residential customers face the decision of whether to invest in DERs, this study aims to be a key tool in aiding that decision-making process.

14 SOLAR ENERGY↗

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT)

The National Renewable Energy Laboratory (NREL) developed the Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT) to allow stakeholders to estimate the long-term aggregate electricity consumption impacts of various energy efficiency measures in Puerto Rico’s residential, commercial, and industrial sectors. PREESAT is an energy accounting framework that addresses electricity use. This report provides an overview of the tool and serves as a user manual with instructions for users.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT)

The National Renewable Energy Laboratory (NREL) developed the Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT) to allow stakeholders to estimate the long-term aggregate electricity consumption impacts of various energy efficiency measures in Puerto Rico’s residential, commercial, and industrial sectors. PREESAT is an energy accounting framework that addresses electricity use. These presentation slides provide a summary of the electrical sector in Puerto Rico and an overview of how the PREESAT tool can be used to project energy savings from various energy efficiency measures in the residential, commercial, and industrial sectors.

commercial↗

Demonstration of Intelligent HVAC Load Management With Deep Reinforcement Learning: Real-World Experience of Machine Learning in Demand Control

We report that buildings account for 40% of total primary energy consumption and 30% of all CO 2 emissions worldwide. A large portion of building energy consumption is due to heating, ventilation, and air-conditioning (HVAC) systems. In the summer, for example, more than 50% of a building’s electricity consumption is used for cooling. With proper energy management, buildings can provide load shifting, peak shaving, frequency regulation, and many other demand response services.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗