dsgrid: Demand-Side Grid Model

NREL's demand-side grid (dsgrid) model 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.

Electricity Load Modeling

Graphic showing the components of the demand-side grid (dsgrid) model, including the spatial resolution across the United States; sectoral resolution incorporating different models for residential; commercial, industrial, and transportation loads; and temporal resolution including yearly and hourly data on electricity load.

Electrical load forecasting has been at the heart of utility planning for decades, but typical top-down approaches do not provide the granularity in time, geography, end use, and technology that is needed to explore the potential impact of technological shifts. For example, many studies of future grid systems employ relatively simple scaling of historical loads, placing more emphasis on supply-side resources such as generation and transmission that are fewer in number and are better understood in terms of cost and performance as compared to demand-side resources.

The dsgrid platform fills these gaps with a bottom-up methodology that allows analysis of "what if" scenarios of future electricity load. It leverages multiple detailed sectoral energy models to provide hourly time series of load by subsector, end use, and U.S. county. Each dsgrid data set typically covers a full year, which either represents a historical year or a future model-year scenario.

Although dsgrid currently emphasizes electricity load data, its component sector models for residential buildings, commercial buildings, and industry provide information on other fuel use, including natural gas. The data sets can be leveraged to support analysis of numerous demand-side technology-driven changes, such as energy efficiency, electrification, and operational flexibility (i.e., demand response). The electricity use data are time-synchronized with solar and wind data sets and are thus suitable for use in power systems analysis.

Component models include:

  • Residential Sector
    ResStock™ is a physics-based simulation model developed to represent the energy use and energy saving potential of residential building stocks with high granularity at national, regional, and local scales. ResStock is a U.S. Department of Energy (DOE) model that has been developed and maintained by NREL since 2014. It takes a new approach to large-scale residential energy analysis by combining large public and private data sources, statistical sampling, detailed subhourly building simulations, and high-performance computing.

  • Commercial Sector
    ComStock™ is a DOE model of the U.S. commercial building stock, developed and maintained by NREL. The model takes some building characteristics from the DOE's Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics.

  • Transportation Sector
    TEMPO™ is an NREL model used to explore transportation pathway options to produce long-term scenarios that reach strategic transportation-energy-environment objectives and assess synergies with energy supply. TEMPO is used to project mobility demand for passenger and freight, mode choices, technology adoption and ultimately energy use and emissions, including annual hourly charging load profiles within each county. TEMPO is currently used in dsgrid to project county-level hourly load profiles associated with personal light-duty electric vehicle charging.

  • Industrial Sector
    NREL initially partnered with Oak Ridge National Laboratory and the Electric Power Research Institute to model industrial energy use with the Industrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E) for the Electrification Futures Study. NREL is currently working to understand how industry may evolve to use more renewable energy, electrify, and otherwise decarbonize. These efforts have focused on first improving the geographic, operational, and temporal detail of industrial energy data. More information on this work can be found in the analysis done for the Solar for Industrial Process Heat project, NREL’s United States county-level industrial energy use data, and in the Applied Energy article, Using facility-level emissions data to estimate the technical potential of alternative thermal sources to meet industrial heat demand.

dsgrid-Supported Studies

Illustration showing various electricity consumers (e.g., buildings) along an electrical cord emanating from various power sources (e.g., a wind turbine), with an outline of the contiguous United States as a background.

Electrification Futures Study

The Electrification Futures Study (EFS) was a multi-year research project that explored the impacts of widespread electrification on all U.S. economic sectors. The dsgrid model was developed for the EFS and used to produce hourly electricity consumption profiles for every county in the contiguous United States for historical year 2012. The initial modeling methods and results are publicly available and described in the dsgrid model documentation and on the EFS website.


The Los Angeles cityscape at sunset.

Los Angeles 100% Renewable Energy Study

A city-scale version of dsgrid was developed for NREL’s groundbreaking Los Angeles 100% Renewable Energy Study (LA100). LA100 involved a series of integrated modeling activities to explore pathways to the nation’s second-largest city could take to achieve their goal of a 100% clean energy future. The dsgrid model was used to inform bulk power system modeling, customer adoption of distributed PV and storage, distribution system modeling, and air quality modeling. More information can be found in the executive summary of the final LA100 report, chapter 3: electricity demand projections, and chapter 12: synthesis.


Open-Source API Access

The dsgrid data produced for the EFS is available via the NREL Open Energy Data Initiative (OEDI) in an HDF5 format. The open source Python API is available through Github and PyPI. Example Jupyter notebooks demonstrate how to access, summarize, and visualize the data on OEDI using the API.

The dsgrid team is also hard at work on a new data format and API, which were designed in response to lessons learned during dsgrid's first two projects. A new national-scale dataset is expected to be published by Spring 2022. 

Get the dsgrid-legacy-efs-api for browsing and analyzing the EFS data files:

GitHub

PyPI

Get the dsgrid EFS data files:

OEDI Catalog Entry

OEDI Data Browser

Publications

Executive Summary in The Los Angeles 100% Renewable Energy Study, NREL Technical Report (2021)

Chapter 3: Electricity Demand Projects in The Los Angeles 100% Renewable Energy Study, NREL Technical Report (2021)

Chapter 12: Synthesis in The Los Angeles 100% Renewable Energy Study, NREL Technical Report (2021)

The Demand-Side Grid (dsgrid) Model Documentation, NREL Technical Report (2018)

The demand-side grid (dsgrid) model, Presentation (2018)

Demand Response Resource Quantification with Detailed Building Energy Models, Presentation (2016)

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