Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Yip, Arthur; Hoehne, Christopher; Jadun, Paige; Ledna, Catherine; Hale, Elaine; Muratori, Matteo; Thom, Daniel; Mooney, Meghan; Liu, Lixi

doi:10.25984/2373091

Title: Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Dataset
Other Related Research

Abstract

Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. Thismore »« less

Authors:

;

; Jadun, Paige;

;

; Thom, Daniel;

; Liu, Lixi

National Renewable Energy Laboratory

Publication Date:: Tue Aug 29 04:00:00 UTC 2023

Other Number(s):: 5958

Research Org.:: DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Multiple Programs (EE)

Collaborations:: National Renewable Energy Laboratory

Subject:: Array; EV; TEMPO; United States; bulk power systems; charging load; computational science; demand model; demand-side grid; dsgrid; electric vehicle; electric vehicle charging; electrification; energy; energy modeling; long-term load forecasting; model; passenger vehicles; power; projection; transportation

OSTI Identifier:: 2373091

DOI:: https://doi.org/10.25984/2373091

Citation Formats


                    Yip, Arthur, Hoehne, Christopher, Jadun, Paige, Ledna, Catherine, Hale, Elaine, Muratori, Matteo, Thom, Daniel, Mooney, Meghan, and Liu, Lixi. Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022.  United States: N. p., 2023. 
        Web.  doi:10.25984/2373091.

Copy to clipboard


                    Yip, Arthur, Hoehne, Christopher, Jadun, Paige, Ledna, Catherine, Hale, Elaine, Muratori, Matteo, Thom, Daniel, Mooney, Meghan, & Liu, Lixi. Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022.  United States.  doi:https://doi.org/10.25984/2373091

Copy to clipboard


                    Yip, Arthur, Hoehne, Christopher, Jadun, Paige, Ledna, Catherine, Hale, Elaine, Muratori, Matteo, Thom, Daniel, Mooney, Meghan, and Liu, Lixi. 2023.  
"Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022".  United States.  doi:https://doi.org/10.25984/2373091.  https://www.osti.gov/servlets/purl/2373091. Pub date:Tue Aug 29 04:00:00 UTC 2023

Copy to clipboard


                    
@article{osti_2373091,

  title        = {Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022},

  author       = {Yip, Arthur and Hoehne, Christopher and Jadun, Paige and Ledna, Catherine and Hale, Elaine and Muratori, Matteo and Thom, Daniel and Mooney, Meghan and Liu, Lixi},

  abstractNote = {Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.},

  doi          = {10.25984/2373091},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Aug 29 04:00:00 UTC 2023},

  month        = {Tue Aug 29 04:00:00 UTC 2023}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.25984/2373091

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS)

Dataset Hale, Elaine ; Horsey, Henry ; Johnson, Brandon ; ...

This data set contains the full-resolution and state-level data described in the linked technical report (https://www.nrel.gov/docs/fy18osti/71492.pdf). It can be accessed with the NREL-dsgrid-legacy-efs-api, available on GitHub at https://github.com/dsgrid/dsgrid-legacy-efs-api and through PyPI (pip install NREL-dsgrid-legacy-efs-api). The data format is HDF5. The API is written in Python. This initial dsgrid data set, whose description was originally published in 2018, covers electricity demand in the contiguous United States (CONUS) for the historical year of 2012. It is a proof-of-concept demonstrating the feasibility of reconciling bottom-up demand modeling results with top-down information about electricity demand to create a more detailed description than is possiblemore »« less
Data Files for “The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure"

Dataset Borlaug, Brennan ; Wood, Eric

This data set includes modeling results from “The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure” including region-specific [i.e., national, state, and core-based statistical area (CBSA)—cities/towns] electric vehicle supply equipment (EVSE) port count requirements in 2025 and 2030 for multiple scenarios described in the study. Please cite as: Wood, E., B. Borlaug, M. Moniot, D.-Y. Lee, Y. Ge, F. Yang, and Z. Liu. 2023. The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5400-85654.
Data Files for 'The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure'

Dataset Wood, Eric ; Borlaug, Brennan

This data set includes modeling results from The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure, including region-specific (i.e., national, state, and core-based statistical area cities and towns) electric vehicle supply equipment port count requirements in 2025 and 2030 for multiple scenarios described in the study.
The Demand-Side Grid (dsgrid) Model

Technical Report Hale, Elaine T.

This presentation gives a high-level overview of the recently published demand-side grid (dsgrid) model documentation. The general modeling philosophy and technical approach of dsgrid is described, as is an initial data set covering 2012 contiguous United States (CONUS) electricity demand. The initial data set describes about 80% of electrical load at an hourly level by county, subsector and end use. Coarser models and data sets are used to describe the remainder. Model residuals are calculated at different levels of aggregation by leveraging historical electricity sector data and a model of 2012 distributed generation. Overall, the dsgrid model has been developedmore »« less
The Demand-Side Grid (dsgrid) Model Documentation

Technical Report Hale, Elaine ; Horsey, Henry ; Johnson, Brandon ; ...

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supply-demand balance that must be maintained on electrical grids at all times and for all timescales, load is a major source of variability and uncertainty in grid operations. Despite this, utility load forecasting is typically done in a top-down manner that lacks the granularity in time, geography, end use, and technology that is needed to explore the potential impact of technological shifts; and many studies of future grid systems employ relatively simple scaling of historical loads, placing more emphasis on supply-side resources such asmore »« less

Similar Records