skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Distributed Generation Market Demand Model (dGen): Documentation

Abstract

The Distributed Generation Market Demand model (dGen) is a geospatially rich, bottom-up, market-penetration model that simulates the potential adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the continental United States through 2050. The National Renewable Energy Laboratory (NREL) developed dGen to analyze the key factors that will affect future market demand for distributed solar, wind, storage, and other DER technologies in the United States. The new model builds off, extends, and replaces NREL's SolarDS model (Denholm et al. 2009a), which simulates the market penetration of distributed PV only. Unlike the SolarDS model, dGen can model various DER technologies under one platform--it currently can simulate the adoption of distributed solar (the dSolar module) and distributed wind (the dWind module) and link with the ReEDS capacity expansion model (Appendix C). The underlying algorithms and datasets in dGen, which improve the representation of customer decision making as well as the spatial resolution of analyses (Figure ES-1), also are improvements over SolarDS.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S); Office of Strategic Programs
OSTI Identifier:
1239054
Report Number(s):
NREL/TP-6A20-65231
DOE Contract Number:
AC36-08GO28308
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; dGen; documentation; distributed energy resources; DERs; technology adoption; SolarDS

Citation Formats

Sigrin, Benjamin, Gleason, Michael, Preus, Robert, Baring-Gould, Ian, and Margolis, Robert. Distributed Generation Market Demand Model (dGen): Documentation. United States: N. p., 2016. Web. doi:10.2172/1239054.
Sigrin, Benjamin, Gleason, Michael, Preus, Robert, Baring-Gould, Ian, & Margolis, Robert. Distributed Generation Market Demand Model (dGen): Documentation. United States. doi:10.2172/1239054.
Sigrin, Benjamin, Gleason, Michael, Preus, Robert, Baring-Gould, Ian, and Margolis, Robert. Mon . "Distributed Generation Market Demand Model (dGen): Documentation". United States. doi:10.2172/1239054. https://www.osti.gov/servlets/purl/1239054.
@article{osti_1239054,
title = {Distributed Generation Market Demand Model (dGen): Documentation},
author = {Sigrin, Benjamin and Gleason, Michael and Preus, Robert and Baring-Gould, Ian and Margolis, Robert},
abstractNote = {The Distributed Generation Market Demand model (dGen) is a geospatially rich, bottom-up, market-penetration model that simulates the potential adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the continental United States through 2050. The National Renewable Energy Laboratory (NREL) developed dGen to analyze the key factors that will affect future market demand for distributed solar, wind, storage, and other DER technologies in the United States. The new model builds off, extends, and replaces NREL's SolarDS model (Denholm et al. 2009a), which simulates the market penetration of distributed PV only. Unlike the SolarDS model, dGen can model various DER technologies under one platform--it currently can simulate the adoption of distributed solar (the dSolar module) and distributed wind (the dWind module) and link with the ReEDS capacity expansion model (Appendix C). The underlying algorithms and datasets in dGen, which improve the representation of customer decision making as well as the spatial resolution of analyses (Figure ES-1), also are improvements over SolarDS.},
doi = {10.2172/1239054},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Feb 01 00:00:00 EST 2016},
month = {Mon Feb 01 00:00:00 EST 2016}
}

Technical Report:

Save / Share: