Resource Planning Model : an integrated resource planning and dispatch tool for regional electric systems /

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Bibliographic Details
Author / Creator:Mai, Trieu, author.
Imprint:Golden, Colorado : National Renewable Energy Laboratory, 2013.
Description:1 online resource (vii, 61 pages) : illustrations (chiefly color).
Language:English
Series:NREL/TP ; 6A20-56723
NREL/TP ; 6 A 20-56723.
Subject:
Format: E-Resource U.S. Federal Government Document Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/9785973
Hidden Bibliographic Details
Varying Form of Title:Integrated resource planning and dispatch tool for regional electric systems
Other authors / contributors:National Renewable Energy Laboratory (U.S.), issuing body.
Notes:Title from title screen (viewed on May 22, 2013).
"January 2013."
Includes bibliographical references (pages 37-40).
Technical report.
DE-AC36-08GO28308 DRS8.3030
Summary:This report introduces a new capacity expansion model, the Resource Planning Model (RPM), with high spatial and temporal resolution that can be used for mid- and long-term scenario planning of regional power systems. Although RPM can be adapted to any geographic region, the report describes an initial version of the model adapted for the power system in Colorado. It presents examples of scenario results from the first version of the model, including an example of a 30%-by-2020 renewable electricity penetration scenario.
GPO item no.:0430-P-03 (online)
Govt.docs classification:E 9.16:NREL/TP-6 A 20-56723

MARC

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500 |a "January 2013." 
504 |a Includes bibliographical references (pages 37-40). 
520 3 |a This report introduces a new capacity expansion model, the Resource Planning Model (RPM), with high spatial and temporal resolution that can be used for mid- and long-term scenario planning of regional power systems. Although RPM can be adapted to any geographic region, the report describes an initial version of the model adapted for the power system in Colorado. It presents examples of scenario results from the first version of the model, including an example of a 30%-by-2020 renewable electricity penetration scenario. 
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