<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T21:52:57Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/62773" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/62773</identifier><datestamp>2026-06-06T01:05:02Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Stephen Connors.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Sachs, Gregory (Gregory Dennis)</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Engineering Systems Division.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Engineering Systems Division</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2011-05-09T15:33:22Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2011-05-09T15:33:22Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2010</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2010</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/62773</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">718747844</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (p. 81).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">A strong and growing desire exists, throughout society, to consume electricity from clean and renewable energy sources, such as solar, wind, biomass, geothermal, and others. Due to the intermittent and variable nature of electricity from these sources, our current electricity grid is incapable of collecting, transmitting, and distributing this energy effectively. The "Smart Grid" is a term which has come to represent this 'next generation' grid, capable of delivering, not only environmental benefits, but also key economic, reliability and energy security benefits as well. Due to the high complexity of the electricity grid, a principle based System Architecture framework is presented as a tool for analyzing, defining, and outlining potential pathways for infrastructure transformation. Through applying this framework to the Smart Grid, beneficiaries and stakeholders are identified, upstream and downstream influences on design are analyzed, and a succinct outline of benefits and functions is produced. The first phase of grid transformation is establishing a robust communications and measurement network. This network will enable customer participation and increase energy efficiency through smart metering, real time pricing, and demand response programs. As penetration of renewables increases, the high variability and uncontrollability of additional energy sources will cause significant operation and control challenges. To mitigate this variability reserve margins will be adjusted and grid scale energy storage (such as compressed air, flow batteries, and plugin hybrid electric vehicles or PHEV's) will begin to be introduced. Achieving over 15% renewable energy penetration marks the second phase of transformation. The third phase is enabling mass adoption, whereby over 40% of our energy will come from renewable sources. This level of penetration will only be achieved through fast supply and demand balancing controls and large scale storage. Robust modeling must be developed to test various portfolio configurations.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Gregory Sachs.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M. in Engineering and Management</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">102 p.</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">M.I.T. theses are protected by 
copyright. They may be viewed from this source for any purpose, but 
reproduction or distribution in any format is prohibited without written 
permission. See provided URL for inquiries about permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Engineering Systems Division.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">A principle based system architecture framework applied for defining, modeling &amp; designing next generation smart grid systems</dim:field>
   <dim:field mdschema="dc" element="type" lang="en_US">Thesis</dim:field>
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	&lt;Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843&lt;/Type>
	&lt;Language>eng&lt;/Language>
   	&lt;Title>A principle based system architecture framework applied for defining, modeling &amp;amp; designing next generation smart grid systems&lt;/Title>
   	&lt;PublishedIn>
    	&lt;Publication>
      	&lt;/Publication>
   	&lt;/PublishedIn>
   	&lt;PublicationDate>2010&lt;/PublicationDate>
   	&lt;Authors>
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        	&lt;DisplayName>Sachs, Gregory (Gregory Dennis)&lt;/DisplayName>
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            &lt;DisplayName>Massachusetts Institute of Technology&lt;/DisplayName>
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    &lt;License>http://dspace.mit.edu/handle/1721.1/7582&lt;/License>
    &lt;Keyword>Engineering Systems Division.&lt;/Keyword>
   	&lt;Abstract>A strong and growing desire exists, throughout society, to consume electricity from clean and renewable energy sources, such as solar, wind, biomass, geothermal, and others. Due to the intermittent and variable nature of electricity from these sources, our current electricity grid is incapable of collecting, transmitting, and distributing this energy effectively. The &amp;quot;Smart Grid&amp;quot; is a term which has come to represent this &amp;apos;next generation&amp;apos; grid, capable of delivering, not only environmental benefits, but also key economic, reliability and energy security benefits as well. Due to the high complexity of the electricity grid, a principle based System Architecture framework is presented as a tool for analyzing, defining, and outlining potential pathways for infrastructure transformation. Through applying this framework to the Smart Grid, beneficiaries and stakeholders are identified, upstream and downstream influences on design are analyzed, and a succinct outline of benefits and functions is produced. The first phase of grid transformation is establishing a robust communications and measurement network. This network will enable customer participation and increase energy efficiency through smart metering, real time pricing, and demand response programs. As penetration of renewables increases, the high variability and uncontrollability of additional energy sources will cause significant operation and control challenges. To mitigate this variability reserve margins will be adjusted and grid scale energy storage (such as compressed air, flow batteries, and plugin hybrid electric vehicles or PHEV&amp;apos;s) will begin to be introduced. Achieving over 15% renewable energy penetration marks the second phase of transformation. The third phase is enabling mass adoption, whereby over 40% of our energy will come from renewable sources. This level of penetration will only be achieved through fast supply and demand balancing controls and large scale storage. Robust modeling must be developed to test various portfolio configurations.&lt;/Abstract>
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