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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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DOE OSTI2021

HDEV Depot Load Profile Generation Code (Code to Generate Heavy-Duty Electric Truck Depot Load Profiles) [SWR-21-72]

Code developed to generate heavy-duty electric truck depot load profiles for the study, "Heavy-Duty Truck Electrification and the Impacts of Depot Charging on Electricity Distribution Systems", by Borlaug et al., published in 2021. This software is provided as-is without dedicated support. The programming environment for this study may be reproduced with conda (installed via the Anaconda website): conda env create -f environment.yml To activate the environment: conda activate hdev-depot-charging-2021

Borlaug, Brennan↗

DOE OSTI2025

Validation of a Co-Optimized, Smart Hybrid Heat Pump Control

This study examines a residential dual fuel (DF) heating system that integrates heat pump and gas furnace technology with a home’s forced-air distribution ducts. Specifically, it explores a retrofit where an “add-on” heat pump replaces the air conditioning (A/C) system and is combined with the home’s existing furnace and air handler. The research team developed and validated an innovative control scheme for this integrated DF system that selected the more economical heating source (furnace or heat pump) while maintaining comfort in the home. The project’s field study and subsequent modeling analysis show that this control scheme can decrease energy use, cost, and emissions compared to a furnace-only system. As homes in mixed and cold climates transition to more electric space heating, furnace-to-dual fuel system retrofits offer a solution that can alleviate the retrofit costs, energy cost concerns, and peak load impacts of full electrification.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Assessing Geospatial and Seasonal Influences on Energy and Cost-Efficiency of Drayage Trucks

The electrification of heavy-duty vehicles is a critical pathway toward improved energy efficiency in the freight sector. The current battery electric truck technology poses several challenges to commercial vehicle operations, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assessing the feasibility of fleet electrification and its impact on the electric grid. This article focuses on developing a model-based simulation approach to predict and analyze the energy consumption of electric trucks by considering the impact of weather and geographical conditions on vehicle road load and auxiliary components power consumption, as well as the impact these factors have on driving range. Specifically, drayage trucks employed in logistics around maritime ports are used as a case study, with consideration of seasonal climate variations and geographical characteristics at different locations. The article includes results for three major container ports within the United States, providing region-specific insights into the energy requirements and driving range of the electric drayage trucks in these regions, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

DOE OSTI2022

E-transit-bench: simulation platform for analyzing electric public transit bus fleet operations

When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid's power flow and therefore cannot account for the power grid's needs in its day-to-day operation. In this paper we propose a framework of transportation-grid co-simulation, analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day's traffic from Chattanooga city's transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of transit electrification that further necessitates such an integrated transportation-grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.

Sen, Rishav↗

DOE OSTI2021

Resilience in an Age of Increasing Electrification

The purpose of this paper is to outline considerations of global electrification trends that include the resultant system-wide and nation-wide impacts. How we incorporate resilience into the electrification plans that transition a nation from conventional energy sources to secure clean energy sources can impact the success of those transitions. Electrification of energy systems has the potential to impact power system planning, emissions, and security; however, little research has been done on comprehensive solutions for incorporating resilience into the electrification process. Research has been conducted on aspects of the energy system and electrification, but not holistically across disciplines and related to the interdependencies of different components of the grid system. This gap in research provides an opportunity for more analysis and a collection of best practices to incorporate resilience into electrification plans. Although clean energy is the goal of an increasing number of nations worldwide, there are many considerations in designing clean energy transitions and increased electrification. Resilience must be factored into clean energy transitions from planning, market design, and policies to modernized grid structures and new, location-specific dispersed energy and energy storage technologies.

clean energy↗

DOE OSTI2023

Quick Start to Electrifying Your School Bus Fleet

Learn how electric school buses can work for your district by understanding the basics of electrification. Understand what is needed to electrify your fleet, how much it costs, and how the process will effect your greenhouse gas emission impacts.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Electrification Futures Study Flexible Load Profiles

This data set includes hourly profiles for flexible load developed for the Electrification Futures Study (EFS). The load profiles represent projected end-use electricity demand that is assumed to be flexible (i.e., can be shifted throughout a day) for various scenarios of flexibility (Base, Enhanced), electrification (Reference, Medium, High), and technology advancement (Slow, Moderate, Rapid), and were developed as inputs into the ReEDS model. The quantity of flexible load is estimated using assumptions on the level of flexibility and customer participation within each subsector modeled in the EFS. Detailed assumptions and modeling implementation will be documented in ongoing EFS analyses. Flexible load profiles are provided for a subset of years (2018, 2020, 2024, 2030, 2040, 2050) and are aggregated to the state and sector level. Total electricity load profiles can be found in a related EFS data set (https://dx.doi.org/10.7799/1593122). NOTE: Due to the file size, Mac users may experience issues decompressing the zip files using the Mac Archive Utility. In those cases, decompressing using the command line is recommended. - Mai, Trieu, Paige Jadun, Jeffrey Logan, Colin McMillan, Matteo Muratori, Daniel Steinberg, Laura Vimmerstedt, Ryan Jones, Benjamin Haley, and Brent Nelson. 2018. Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. National Renewable Energy Laboratory. NREL/TP-6A20-71500. https://doi.org/10.2172/1459351. - Murphy, Caitlin, Trieu Mai, Yinong Sun, Paige Jadun, Matteo Muratori, Brent Nelson, Ryan Jones. Forthcoming. Electrification Futures Study: Scenarios of Power System Evolution and Infrastructure Development for the United States. National Renewable Energy Laboratory. - Sun, Yinong, Paige Jadun, Brent Nelson, Matteo Muratori, Caitlin Murphy, Jeffrey Logan, and Trieu Mai. Forthcoming. Electrification Futures Study: Methodological Approaches for Assessing Long-Term Power System Impacts of End-Use Electrification. National Renewable Energy Laboratory.

buildings↗

DOE OSTI2023

Commercial Vehicle Electrification Evaluation Toolkit (CVEET)

The CVEET can identify and characterize charging and battery capacity challenges for class 3-8 commercial vehicle electrification, including battery electric and plug in hybrid vehicles for various trucks, vans and buses, over a wide range of vehicle operation. The tool can help build the understanding of the impact of commercial vehicle electrification on energy consumption, payload penalty, and GHG (i.e., CO2) emissions. The tool employs a quantitative approach to estimate electric vehicle energy consumption, characterize component performance and determine appropriate charging assumptions to conduct a comprehensive priority analysis of MD/HD vehicle electrification using public domain data.

Gao, Zhiming↗

DOE OSTI2022

Achieving 50% Energy Savings in Chicago Homes: A Case Study for Advancing Equity and Climate Goals

Since 2019, the National Renewable Energy Laboratory (NREL) and Elevate have collaborated to identify pathways to deep energy retrofits in Chicago's housing stock, document equity implications and co-benefits of this transition, and validate the findings by implementing retrofits in real Chicago homes. This document summarizes our analysis process to model advanced retrofit packages that lead to greater than 50% energy savings in Chicago homes. Based on these findings, we have also developed a roadmap with the City to guide implementation, and are deploying the recommended retrofit packages in real Chicago homes to realize these energy savings. This work was developed in collaboration with two key stakeholders - the City of Chicago and Commonwealth Edison (ComEd) - and funded by the U.S. Department of Energy (DOE). NREL's Residential Buildings team maintains the best-in-class ResStock™ energy model of the U.S. residential building stock. For this work, we calibrated ResStock to Chicago's unique local housing stock to accurately simulate energy use in Chicago homes both for current conditions and with various retrofit scenarios. We simulated a wide range of potential building retrofits covering all aspects of residential energy use and then grouped these into packages based on energy and utility bill savings. ResStock can model diverse building types and housing characteristics, so we're able to observe the range of outcomes that might occur when these upgrades are deployed across the entire housing stock. We can then estimate potential energy savings from an advanced retrofit program on Chicago's housing stock by comparing the modeled energy use before versus after a retrofit. This novel version of ResStock, calibrated to Chicago with data from Elevate, can help City officials, ComEd, and other partners plan for community-scale decarbonization via residential retrofits. Specifically, this work contributes the following project goals: Develop a building retrofit prioritization strategy for Chicago single-family and 2- to 4-unit buildings; Identify neighborhoods and home types that have the highest potential for savings from electrification; and Assess the impact of advanced building retrofits on energy use, utility bills, and CO2 emissions at the city and building level. Although this study is specific to Chicago, its methods and learnings are applicable across the United States. These findings are especially notable for heat pumps and electrification retrofits in cold climates.

building energy modeling↗

DOE OSTI2024

Technical Impacts of Light-Duty and Heavy-Duty Transportation Electrification on a Coordinated Transmission and Distribution System

In this study, we propose a strategy to model the required spatiotemporal charging demand from light-duty (LD) and medium- and heavy-duty (MHD) electric vehicles (EVs) using actual transportation data by mapping the demand for the required EV charging to a realistic and coordinated distribution and transmission electric grid at the predicted times of the day to study their impact on the power system in a variety of load, weather, and EV penetration scenarios. This work is the first study that includes the actual weather data and transportation data with realistic and coordinated distribution and transmission grid data in a large industry-scale level study. The main goal of this study is to identify possible issues and required upgrades in the electric grid, caused by an increase in EV integration. The transmission case study is a large grid with 6717 buses over a Texas footprint, and the distribution grid is over Houston, a city in Texas, covering over three million customers. The resulting overloads and voltage violations experienced in the system are discussed, and required planning upgrades to avoid these issues are suggested.

AC optimal power flow (AC-OPF)↗

DOE OSTI2022

The impact of energy-efficiency upgrades and other distributed energy resources on a residential neighborhood-scale electrification retrofit

We report ambitious targets for carbon emissions reductions are highlighting new challenges for electrification strategies, leading to an increased focus on building load flexibility and energy management to complement the variability inherent in renewable energy generation. Over the next decade millions of existing homes could undergo electrification retrofits, and there is an urgent need to understand the potential impacts of electrifying major residential loads such as water and space heating on community load characteristics, resident energy bills, and the utility's distribution system. Behind-the-meter distributed energy resources (DERs), including efficiency measures, photovoltaics (PV), battery storage, managed electric vehicle (EV) charging, and controls such as home energy management systems (HEMS), can significantly alter a neighborhood's load profile and provide benefits to both the residents and the grid. We present a novel approach to characterizing the impact of a hypothetical neighborhood-scale residential retrofit program on individual homes' energy use profiles, associated utility bills, and the local distribution system. We modeled a mixed-fuel community of 30 single-family homes in Denver, Colorado, and compared the effects of retrofit scenarios ranging from conventional energy-efficiency upgrades to full electrification with and without more advanced DER technologies. We analyzed which packages of DERs most reliably enable demand flexibility in response to a time-of-use (TOU) rate for this and similar neighborhoods. Our buildings-to-grid co-simulation framework includes a generic secondary distribution feeder model to capture voltage profiles, transformer loading, and other grid impacts in each case. We also calculated the carbon emissions associated with energy use in the community. The methodology developed here can be broadly applied to community-scale beneficial electrification studies in other regions, climates, utility infrastructures, and building typologies to make specific, targeted recommendations based on quantified projections of energy demand in any given community. Our findings indicate that residential electrification can be achieved without negatively impacting the monthly utility bill, and that a combination of conventional energy-efficiency measures, PV, battery, controls, and managed EV charging to maximize a community's demand flexibility is a promising strategy. Adding DERs (especially PV) as part of efficient electrification produces much bigger savings than efficient electrification without DERs. A key barrier is that upgrades require upfront costs, and modest utility bill savings result in long payback periods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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 changes in peak demand from building and transportation electrification with energy efficiency [Slides]

The Department of Energy funded Berkeley Lab to provide technical assistance to two municipal utilities on how energy efficiency and demand flexibility can mitigate the peak demand impacts of building and transportation electrification. Berkeley Lab worked with these utilities, Sacramento Municipal Utility District (SMUD) and Fort Collins Utilities, to identify research questions that supported their planning needs. For both utilities, Berkeley Lab developed scenario-based load forecasts that considered baseline and high-efficiency building electrification. For SMUD, the forecast also explored the sensitivity of peak demand to extreme weather (a winter cold snap) at the system-level. For Fort Collins Utilities, the forecast addressed the impacts of low, medium, and high levels of building and transportation technology adoption on select distribution feeders. Berkeley Lab is also developing a guidance document for utilities that will draw on lessons learned from the technical assistance and provide a framework for conducting similar analyses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

ResStock Technical Reference Documentation (V.3.3.0)

ResStock™ is the best-in-class building stock energy model for simulating and publishing energy use, utility bills, and greenhouse gas emissions from the residential sector of the U.S. ResStock answers two primary questions: (1) How is energy used in the U.S. residential building stock? and (2) What is the timeseries aggregate impact of energy technologies? Specifically, ResStock quantifies energy use across geographical locations, demographic groups, building types, fuels, end uses, and time of day. Additionally, it details the impact of efficiency or electrification measures: total changes in the amount of energy used by measure; where or in what use cases efficiency or electrification upgrade measures save energy; when or at what times of day savings occur; and which building stock or demographic segments have the biggest savings potential. This model, and the datasets it produces, are foundational to identifying pathways to affordable and equitable decarbonization of the U.S. residential buildings sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Insights from NREL’s Electrification Futures and LA100 Studies

Electrification of end-use technologies could have important implications for bulk power sector planning, operations, and impacts. By combining previously-defined electrification scenarios with capacity expansion modeling, we evaluate the potential challenges and opportunities associated with electrification. Our scenario analysis reveals that electrification reduces energy consumption and air emissions from across the energy sector, and its impacts on energy system costs are modest due, in part, to the abundance of low-cost resources in the United States.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Electrification Futures Study: Scenarios of Power System Evolution

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Equitable Electrification Analysis for Existing Buildings in Richmond, CA

Over a 12-month period beginning in July 2022, NREL coordinated with a coalition of staff from the City of Richmond and local community organizations to develop and conduct a city-wide building energy use analysis and develop and assess the impacts of various approaches to electrifying and improving energy efficiency of all existing residential and commercial buildings within the city limits. Building on data available through NREL's ResStock™ and ComStock™ analysis tools, the authors looked at potential modeled impacts of building envelope and electrification upgrades on five indicators identified by the community coalition: building energy consumption, greenhouse gas (GHG) emissions, utility bill charges and cost-effectiveness, employment impacts, and health and safety impacts. This report summarizes the findings of that research and analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Clean Grid Vision: A U.S. Perspective - Chapter 4. Demand-Side Development

This chapter focuses on recent developments on the demand side of the electricity system in the United States and beyond. After decades of slow-moving change in power system technology, business models, regulatory structures, and even financing schemes, changes on the demand side are accelerating rapidly. These changes are also more tightly linking the supply side with the demand side. This chapter provides high-level summaries of traditional subjects like demand-side management, which includes energy efficiency and demand response, and then transition to a subject that has gathered increasing attention as the most dynamic change likely to impact power systems in the coming decades: beneficial electrification. In each case, we attempt to address the impacts on system flexibility to the degree possible. These demand-side subjects are widely considered essential elements of a clean grid strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗