Planning for the evolution of the electric grid with a long-run marginal emission rate - data and code

Gagnon, Pieter; Cole, Wesley

doi:10.7799/1843377

Title: Planning for the evolution of the electric grid with a long-run marginal emission rate - data and code

Dataset
Other Related Research

Abstract

Data and code generated for the paper "Planning for the evolution of the electric grid with a long-run marginal emission rate", Pieter Gagnon and Wesley Cole, 2022.

Authors:

;

Grid Planning and Analysis Center; National Renewable Energy Laboratory
Grid Planning and Analysis Center

Publication Date:: Tue Feb 01 23:00:00 EST 2022

Other Number(s):: FY21 AOP 2.4.0.1

Research Org.:: National Renewable Energy Laboratory - Data (NREL-DATA), Golden, CO (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Org.:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; 25 ENERGY STORAGE; 29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Cambium; LRMER; Long-run; NREL; ReEDS; emissions

OSTI Identifier:: 1843377

DOI:: https://doi.org/10.7799/1843377

Citation Formats


                    Gagnon, Pieter, and Cole, Wesley. Planning for the evolution of the electric grid with a long-run marginal emission rate - data and code.  United States: N. p., 2022. 
        Web.  doi:10.7799/1843377.

Copy to clipboard


                    Gagnon, Pieter, & Cole, Wesley. Planning for the evolution of the electric grid with a long-run marginal emission rate - data and code.  United States.  doi:https://doi.org/10.7799/1843377

Copy to clipboard


                    Gagnon, Pieter, and Cole, Wesley. 2022.  
"Planning for the evolution of the electric grid with a long-run marginal emission rate - data and code".  United States.  doi:https://doi.org/10.7799/1843377.  https://www.osti.gov/servlets/purl/1843377. Pub date:Tue Feb 01 23:00:00 EST 2022

Copy to clipboard


                    
@article{osti_1843377,

  title        = {Planning for the evolution of the electric grid with a long-run marginal emission rate - data and code},

  author       = {Gagnon, Pieter and Cole, Wesley},

  abstractNote = {Data and code generated for the paper "Planning for the evolution of the electric grid with a long-run marginal emission rate", Pieter Gagnon and Wesley Cole, 2022.},

  doi          = {10.7799/1843377},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Feb 01 23:00:00 EST 2022},

  month        = {Tue Feb 01 23:00:00 EST 2022}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.7799/1843377

Save / Share:

Export Metadata

Save to My Library

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

Long-run Marginal Emission Rates for Electricity - Workbooks for 2021 Cambium Data

Dataset Gagnon, Pieter ; Hale, Elaine ; Cole, Wesley

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treatsmore »« less
Long-run Marginal Emission Rates for Electricity - Workbooks for 2022 Cambium Data

Dataset Gagnon, Pieter ; Cowiestoll, Brady ; Schwarz, Marty

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. A LRMER is an estimate of the rate of emissions that would be either induced or avoided by a change in electric demand, taking into account how the change could influence both the operation as well as the structure of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treat grid assets as fixed. Long-run marginal emissions rates are generally appropriate to use when trying to comprehensivelymore »« less
Long-run Marginal Emission Rates for Electricity - Workbooks for 2023 Cambium Data

Dataset Gagnon, Pieter

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States' electric sector. A LRMER is an estimate of the rate of emissions that would be either induced or avoided by a change in electric demand, taking into account how the change could influence both the operation as well as the structure of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). These workbooks provide data for 18 GEA regions covering the contiguous United States. Mappings of these regions to ZIP codes and counties is given in thismore »« less
Planning for the evolution of the electric grid with a long-run marginal emission rate

Journal Article Gagnon, Pieter ; Cole, Wesley - iScience
Planning for the evolution of the electric grid with a long-run marginal emission rate

Journal Article Gagnon, Pieter ; Cole, Wesley - iScience

Emissions factors are widely used to estimate how various interventions would influence emissions from the electric sector. Both of the most commonly used metrics, however, neglect how changes in electricity demand can influence the structural evolution of the grid (the building and retiring of capital assets, such as generators). This omission can be significant when the factors are intended to comprehensively reflect the consequences of an intervention. In this work we evaluate a lesser known metric - the long-run marginal emission rate (LRMER) - which incorporates both the operational and structural implication of changes in electricity demand. We apply amore »« less

Similar Records