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A fuzzy environmental-technical-economic model for distributed generation planning
To determine the optimal size, location and also the proper technology of distributed generation (DG) units in distribution systems, a static fuzzy multiobjective model is proposed in this paper. The proposed model can concurrently optimize a number of conflicting and competing objective functions i...
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Published in: | Energy (Oxford) 2011-05, Vol.36 (5), p.3437-3445 |
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description | To determine the optimal size, location and also the proper technology of distributed generation (DG) units in distribution systems, a static fuzzy multiobjective model is proposed in this paper. The proposed model can concurrently optimize a number of conflicting and competing objective functions including economic, technical and environmental attributes. The economic function is the profit of a distribution company (DisCo) from selling the DG output power to its customers. The contribution of this model is the consideration of some DG marginal revenues in the economic function. Inclusion of marginal revenues would not only reduce the investment risks of DG technologies, but also would enable the optimal penetration of DG units. The proposed DG planning framework considers various DG technologies such as photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), gas turbine (GT) and diesel engine (DE). The system uncertainties (including those for the energy demand, energy price and DG technologies operating and investment costs) are modeled using fuzzy numbers. The numerical case studies have been carried out using the IEEE 37-node distribution test system to demonstrate the performance of the proposed DG planning model.
►Proposing a fuzzy multiobjective framework to find an optimal solution for the locations, sizes and technologies of DGs within distribution system. ►The multiobjective planning framework is based on economic, technical and environmental objectives. ►Fuzzy numbers theory has been applied to model various uncertainties in the planning framework. ►Considering some marginal revenue functions of various DG technologies according to their characteristics and abilities. ►It has been found that renewable technologies need more incentive revenue programs in addition to their inherent marginal revenues to compete with conventional technologies. |
doi_str_mv | 10.1016/j.energy.2011.03.048 |
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►Proposing a fuzzy multiobjective framework to find an optimal solution for the locations, sizes and technologies of DGs within distribution system. ►The multiobjective planning framework is based on economic, technical and environmental objectives. ►Fuzzy numbers theory has been applied to model various uncertainties in the planning framework. ►Considering some marginal revenue functions of various DG technologies according to their characteristics and abilities. ►It has been found that renewable technologies need more incentive revenue programs in addition to their inherent marginal revenues to compete with conventional technologies.</description><identifier>ISSN: 0360-5442</identifier><identifier>DOI: 10.1016/j.energy.2011.03.048</identifier><identifier>CODEN: ENEYDS</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Applied sciences ; case studies ; consumers (people) ; diesel engines ; Distributed generation (DG) ; Distribution power companies (DisCo) ; Economic data ; Economics ; Electric energy ; Energy ; energy costs ; Energy economics ; Energy use ; Energy. Thermal use of fuels ; Engines and turbines ; Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc ; Exact sciences and technology ; Financing ; Fuel cells ; Fuzzy ; Fuzzy numbers ; Fuzzy set theory ; General, economic and professional studies ; income ; Mathematical analysis ; Mathematical models ; multiobjective decision making (MODM) ; Natural energy ; planning ; Revenues ; risk ; wind turbines</subject><ispartof>Energy (Oxford), 2011-05, Vol.36 (5), p.3437-3445</ispartof><rights>2011</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-2bf9a5e5af42075fc35503952ab1cf0e5011a19559afc8fe60d61c1833f107ab3</citedby><cites>FETCH-LOGICAL-c425t-2bf9a5e5af42075fc35503952ab1cf0e5011a19559afc8fe60d61c1833f107ab3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24213210$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Zangeneh, Ali</creatorcontrib><creatorcontrib>Jadid, Shahram</creatorcontrib><creatorcontrib>Rahimi-Kian, Ashkan</creatorcontrib><title>A fuzzy environmental-technical-economic model for distributed generation planning</title><title>Energy (Oxford)</title><description>To determine the optimal size, location and also the proper technology of distributed generation (DG) units in distribution systems, a static fuzzy multiobjective model is proposed in this paper. The proposed model can concurrently optimize a number of conflicting and competing objective functions including economic, technical and environmental attributes. The economic function is the profit of a distribution company (DisCo) from selling the DG output power to its customers. The contribution of this model is the consideration of some DG marginal revenues in the economic function. Inclusion of marginal revenues would not only reduce the investment risks of DG technologies, but also would enable the optimal penetration of DG units. The proposed DG planning framework considers various DG technologies such as photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), gas turbine (GT) and diesel engine (DE). The system uncertainties (including those for the energy demand, energy price and DG technologies operating and investment costs) are modeled using fuzzy numbers. The numerical case studies have been carried out using the IEEE 37-node distribution test system to demonstrate the performance of the proposed DG planning model.
►Proposing a fuzzy multiobjective framework to find an optimal solution for the locations, sizes and technologies of DGs within distribution system. ►The multiobjective planning framework is based on economic, technical and environmental objectives. ►Fuzzy numbers theory has been applied to model various uncertainties in the planning framework. ►Considering some marginal revenue functions of various DG technologies according to their characteristics and abilities. ►It has been found that renewable technologies need more incentive revenue programs in addition to their inherent marginal revenues to compete with conventional technologies.</description><subject>Applied sciences</subject><subject>case studies</subject><subject>consumers (people)</subject><subject>diesel engines</subject><subject>Distributed generation (DG)</subject><subject>Distribution power companies (DisCo)</subject><subject>Economic data</subject><subject>Economics</subject><subject>Electric energy</subject><subject>Energy</subject><subject>energy costs</subject><subject>Energy economics</subject><subject>Energy use</subject><subject>Energy. Thermal use of fuels</subject><subject>Engines and turbines</subject><subject>Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc</subject><subject>Exact sciences and technology</subject><subject>Financing</subject><subject>Fuel cells</subject><subject>Fuzzy</subject><subject>Fuzzy numbers</subject><subject>Fuzzy set theory</subject><subject>General, economic and professional studies</subject><subject>income</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>multiobjective decision making (MODM)</subject><subject>Natural energy</subject><subject>planning</subject><subject>Revenues</subject><subject>risk</subject><subject>wind turbines</subject><issn>0360-5442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kU1LxDAQhntQ8PMfCPYiemmdJE3aXgQRv0AQ_DiHbDpZs7TJmnQX1l9v1opHTzOHZ2ZensmyEwIlASIuFyU6DPNNSYGQElgJVbOT7QMTUPCqonvZQYwLAOBN2-5nL9e5WX19bXJ0axu8G9CNqi9G1B_O6tSh9s4PVueD77DPjQ95Z-MY7Gw1YpfPt9fUaL3Ll71yzrr5UbZrVB_x-LceZu93t283D8XT8_3jzfVToSvKx4LOTKs4cmUqCjU3mnEOrOVUzYg2gDzlV6TlvFVGNwYFdIJo0jBmCNRqxg6z82nvMvjPFcZRDjZq7FMM9Ksom5oSRkndJvLiX5LUdU2gEkIktJpQHXyMAY1cBjuosJEE5FawXMhJsNwKlsBkEpzGzn4vqJismaCctvFvllY_USBxpxNnlJdqHhLz_poWifQQ0TY1ScTVRGBSt7YYZNQWncbOBtSj7Lz9P8o319me4w</recordid><startdate>20110501</startdate><enddate>20110501</enddate><creator>Zangeneh, Ali</creator><creator>Jadid, Shahram</creator><creator>Rahimi-Kian, Ashkan</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7SU</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>7ST</scope><scope>SOI</scope></search><sort><creationdate>20110501</creationdate><title>A fuzzy environmental-technical-economic model for distributed generation planning</title><author>Zangeneh, Ali ; Jadid, Shahram ; Rahimi-Kian, Ashkan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c425t-2bf9a5e5af42075fc35503952ab1cf0e5011a19559afc8fe60d61c1833f107ab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Applied sciences</topic><topic>case studies</topic><topic>consumers (people)</topic><topic>diesel engines</topic><topic>Distributed generation (DG)</topic><topic>Distribution power companies (DisCo)</topic><topic>Economic data</topic><topic>Economics</topic><topic>Electric energy</topic><topic>Energy</topic><topic>energy costs</topic><topic>Energy economics</topic><topic>Energy use</topic><topic>Energy. Thermal use of fuels</topic><topic>Engines and turbines</topic><topic>Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc</topic><topic>Exact sciences and technology</topic><topic>Financing</topic><topic>Fuel cells</topic><topic>Fuzzy</topic><topic>Fuzzy numbers</topic><topic>Fuzzy set theory</topic><topic>General, economic and professional studies</topic><topic>income</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>multiobjective decision making (MODM)</topic><topic>Natural energy</topic><topic>planning</topic><topic>Revenues</topic><topic>risk</topic><topic>wind turbines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zangeneh, Ali</creatorcontrib><creatorcontrib>Jadid, Shahram</creatorcontrib><creatorcontrib>Rahimi-Kian, Ashkan</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zangeneh, Ali</au><au>Jadid, Shahram</au><au>Rahimi-Kian, Ashkan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A fuzzy environmental-technical-economic model for distributed generation planning</atitle><jtitle>Energy (Oxford)</jtitle><date>2011-05-01</date><risdate>2011</risdate><volume>36</volume><issue>5</issue><spage>3437</spage><epage>3445</epage><pages>3437-3445</pages><issn>0360-5442</issn><coden>ENEYDS</coden><abstract>To determine the optimal size, location and also the proper technology of distributed generation (DG) units in distribution systems, a static fuzzy multiobjective model is proposed in this paper. The proposed model can concurrently optimize a number of conflicting and competing objective functions including economic, technical and environmental attributes. The economic function is the profit of a distribution company (DisCo) from selling the DG output power to its customers. The contribution of this model is the consideration of some DG marginal revenues in the economic function. Inclusion of marginal revenues would not only reduce the investment risks of DG technologies, but also would enable the optimal penetration of DG units. The proposed DG planning framework considers various DG technologies such as photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), gas turbine (GT) and diesel engine (DE). The system uncertainties (including those for the energy demand, energy price and DG technologies operating and investment costs) are modeled using fuzzy numbers. The numerical case studies have been carried out using the IEEE 37-node distribution test system to demonstrate the performance of the proposed DG planning model.
►Proposing a fuzzy multiobjective framework to find an optimal solution for the locations, sizes and technologies of DGs within distribution system. ►The multiobjective planning framework is based on economic, technical and environmental objectives. ►Fuzzy numbers theory has been applied to model various uncertainties in the planning framework. ►Considering some marginal revenue functions of various DG technologies according to their characteristics and abilities. ►It has been found that renewable technologies need more incentive revenue programs in addition to their inherent marginal revenues to compete with conventional technologies.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2011.03.048</doi><tpages>9</tpages></addata></record> |
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subjects | Applied sciences case studies consumers (people) diesel engines Distributed generation (DG) Distribution power companies (DisCo) Economic data Economics Electric energy Energy energy costs Energy economics Energy use Energy. Thermal use of fuels Engines and turbines Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc Exact sciences and technology Financing Fuel cells Fuzzy Fuzzy numbers Fuzzy set theory General, economic and professional studies income Mathematical analysis Mathematical models multiobjective decision making (MODM) Natural energy planning Revenues risk wind turbines |
title | A fuzzy environmental-technical-economic model for distributed generation planning |
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