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An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation
► Proposes a stochastic model for optimal energy management. ► Consider uncertainties related to the forecasted values for load demand. ► Consider uncertainties of forecasted values of output power of wind and photovoltaic units. ► Consider uncertainties of forecasted values of market price. ► Prese...
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| Published in: | Applied energy 2012-11, Vol.99, p.455-470 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c399t-a8d1afcb62f167ae4c7e704fb6d0dc627e028fe59952c77033590beb156b00cb3 |
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| cites | cdi_FETCH-LOGICAL-c399t-a8d1afcb62f167ae4c7e704fb6d0dc627e028fe59952c77033590beb156b00cb3 |
| container_end_page | 470 |
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| container_start_page | 455 |
| container_title | Applied energy |
| container_volume | 99 |
| creator | Niknam, Taher Azizipanah-Abarghooee, Rasoul Narimani, Mohammad Rasoul |
| description | ► Proposes a stochastic model for optimal energy management. ► Consider uncertainties related to the forecasted values for load demand. ► Consider uncertainties of forecasted values of output power of wind and photovoltaic units. ► Consider uncertainties of forecasted values of market price. ► Present an improved multi-objective teaching–learning-based optimization.
This paper proposes a stochastic model for optimal energy management with the goal of cost and emission minimization. In this model, the uncertainties related to the forecasted values for load demand, available output power of wind and photovoltaic units and market price are modeled by a scenario-based stochastic programming. In the presented method, scenarios are generated by a roulette wheel mechanism based on probability distribution functions of the input random variables. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. An improved multi-objective teaching–learning-based optimization is implemented to yield the best expected Pareto optimal front. In the proposed stochastic optimization method, a novel self adaptive probabilistic modification strategy is offered to improve the performance of the presented algorithm. Also, a set of non-dominated solutions are stored in a repository during the simulation process. Meanwhile, the size of the repository is controlled by usage of a fuzzy-based clustering technique. The best expected compromise solution stored in the repository is selected via the niching mechanism in a way that solutions are encouraged to seek the lesser explored regions. The proposed framework is applied in a typical grid-connected micro grid in order to verify its efficiency and feasibility. |
| doi_str_mv | 10.1016/j.apenergy.2012.04.017 |
| format | article |
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This paper proposes a stochastic model for optimal energy management with the goal of cost and emission minimization. In this model, the uncertainties related to the forecasted values for load demand, available output power of wind and photovoltaic units and market price are modeled by a scenario-based stochastic programming. In the presented method, scenarios are generated by a roulette wheel mechanism based on probability distribution functions of the input random variables. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. An improved multi-objective teaching–learning-based optimization is implemented to yield the best expected Pareto optimal front. In the proposed stochastic optimization method, a novel self adaptive probabilistic modification strategy is offered to improve the performance of the presented algorithm. Also, a set of non-dominated solutions are stored in a repository during the simulation process. Meanwhile, the size of the repository is controlled by usage of a fuzzy-based clustering technique. The best expected compromise solution stored in the repository is selected via the niching mechanism in a way that solutions are encouraged to seek the lesser explored regions. The proposed framework is applied in a typical grid-connected micro grid in order to verify its efficiency and feasibility.</description><identifier>ISSN: 0306-2619</identifier><identifier>EISSN: 1872-9118</identifier><identifier>DOI: 10.1016/j.apenergy.2012.04.017</identifier><identifier>CODEN: APENDX</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>algorithms ; Applied sciences ; Energy ; Exact sciences and technology ; Improved teaching–learning-based algorithm ; market prices ; Micro grid ; Multi-objective stochastic optimization ; optimization methods ; probability distribution ; Renewable energy management ; Self adaptive probabilistic modification strategy ; solutions ; stochastic models ; stochastic processes ; storing ; system optimization ; Uncertainty ; wind power</subject><ispartof>Applied energy, 2012-11, Vol.99, p.455-470</ispartof><rights>2012 Elsevier Ltd</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-a8d1afcb62f167ae4c7e704fb6d0dc627e028fe59952c77033590beb156b00cb3</citedby><cites>FETCH-LOGICAL-c399t-a8d1afcb62f167ae4c7e704fb6d0dc627e028fe59952c77033590beb156b00cb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,783,787,27936,27937</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26255040$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Niknam, Taher</creatorcontrib><creatorcontrib>Azizipanah-Abarghooee, Rasoul</creatorcontrib><creatorcontrib>Narimani, Mohammad Rasoul</creatorcontrib><title>An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation</title><title>Applied energy</title><description>► Proposes a stochastic model for optimal energy management. ► Consider uncertainties related to the forecasted values for load demand. ► Consider uncertainties of forecasted values of output power of wind and photovoltaic units. ► Consider uncertainties of forecasted values of market price. ► Present an improved multi-objective teaching–learning-based optimization.
This paper proposes a stochastic model for optimal energy management with the goal of cost and emission minimization. In this model, the uncertainties related to the forecasted values for load demand, available output power of wind and photovoltaic units and market price are modeled by a scenario-based stochastic programming. In the presented method, scenarios are generated by a roulette wheel mechanism based on probability distribution functions of the input random variables. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. An improved multi-objective teaching–learning-based optimization is implemented to yield the best expected Pareto optimal front. In the proposed stochastic optimization method, a novel self adaptive probabilistic modification strategy is offered to improve the performance of the presented algorithm. Also, a set of non-dominated solutions are stored in a repository during the simulation process. Meanwhile, the size of the repository is controlled by usage of a fuzzy-based clustering technique. The best expected compromise solution stored in the repository is selected via the niching mechanism in a way that solutions are encouraged to seek the lesser explored regions. The proposed framework is applied in a typical grid-connected micro grid in order to verify its efficiency and feasibility.</description><subject>algorithms</subject><subject>Applied sciences</subject><subject>Energy</subject><subject>Exact sciences and technology</subject><subject>Improved teaching–learning-based algorithm</subject><subject>market prices</subject><subject>Micro grid</subject><subject>Multi-objective stochastic optimization</subject><subject>optimization methods</subject><subject>probability distribution</subject><subject>Renewable energy management</subject><subject>Self adaptive probabilistic modification strategy</subject><subject>solutions</subject><subject>stochastic models</subject><subject>stochastic processes</subject><subject>storing</subject><subject>system optimization</subject><subject>Uncertainty</subject><subject>wind power</subject><issn>0306-2619</issn><issn>1872-9118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNqFkUFP3DAQha2KSl1o_0LrCxKXpGMncZIbCNEWCamHlrPlOOOtt0m82F4q_n0nWuDKaazRmzfP3zD2WUApQKivu9LsccG4fSolCFlCXYJo37GN6FpZ9EJ0J2wDFahCKtF_YKcp7QBACgkbNl8tHJ3z1uOSebK4mOhDMZiEI0852D8mZW_5PoZtNPPsly139MB_If7lLkQ-H6bsizDs0Gb_iDzss5_NxGdvYyi20Y_UwmiyD8tH9t6ZKeGn53rG7r_d_L7-Udz9_H57fXVX2Krvc2G6URhnByWdUK3B2rbYQu0GNcJolWwRZOew6ftG2raFqmp6GHAQjRoA7FCdsYujL8V-OGDKevb0t2kyC4ZD0gKqTpF1BSRVRymlTSmi0_tI-eMTifTKV-_0C1-98tVQa-JLg-fPO0yyZiIoi_XpdVoq2TRQrwu-HHXOBG2IR9L3v8hI0Q1E19er0-VRgYTk0WPUaT2HxdFHgqrH4N8K8x-oH6A4</recordid><startdate>20121101</startdate><enddate>20121101</enddate><creator>Niknam, Taher</creator><creator>Azizipanah-Abarghooee, Rasoul</creator><creator>Narimani, Mohammad Rasoul</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope></search><sort><creationdate>20121101</creationdate><title>An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation</title><author>Niknam, Taher ; Azizipanah-Abarghooee, Rasoul ; Narimani, Mohammad Rasoul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-a8d1afcb62f167ae4c7e704fb6d0dc627e028fe59952c77033590beb156b00cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>algorithms</topic><topic>Applied sciences</topic><topic>Energy</topic><topic>Exact sciences and technology</topic><topic>Improved teaching–learning-based algorithm</topic><topic>market prices</topic><topic>Micro grid</topic><topic>Multi-objective stochastic optimization</topic><topic>optimization methods</topic><topic>probability distribution</topic><topic>Renewable energy management</topic><topic>Self adaptive probabilistic modification strategy</topic><topic>solutions</topic><topic>stochastic models</topic><topic>stochastic processes</topic><topic>storing</topic><topic>system optimization</topic><topic>Uncertainty</topic><topic>wind power</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Niknam, Taher</creatorcontrib><creatorcontrib>Azizipanah-Abarghooee, Rasoul</creatorcontrib><creatorcontrib>Narimani, Mohammad Rasoul</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><jtitle>Applied energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Niknam, Taher</au><au>Azizipanah-Abarghooee, Rasoul</au><au>Narimani, Mohammad Rasoul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation</atitle><jtitle>Applied energy</jtitle><date>2012-11-01</date><risdate>2012</risdate><volume>99</volume><spage>455</spage><epage>470</epage><pages>455-470</pages><issn>0306-2619</issn><eissn>1872-9118</eissn><coden>APENDX</coden><abstract>► Proposes a stochastic model for optimal energy management. ► Consider uncertainties related to the forecasted values for load demand. ► Consider uncertainties of forecasted values of output power of wind and photovoltaic units. ► Consider uncertainties of forecasted values of market price. ► Present an improved multi-objective teaching–learning-based optimization.
This paper proposes a stochastic model for optimal energy management with the goal of cost and emission minimization. In this model, the uncertainties related to the forecasted values for load demand, available output power of wind and photovoltaic units and market price are modeled by a scenario-based stochastic programming. In the presented method, scenarios are generated by a roulette wheel mechanism based on probability distribution functions of the input random variables. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. An improved multi-objective teaching–learning-based optimization is implemented to yield the best expected Pareto optimal front. In the proposed stochastic optimization method, a novel self adaptive probabilistic modification strategy is offered to improve the performance of the presented algorithm. Also, a set of non-dominated solutions are stored in a repository during the simulation process. Meanwhile, the size of the repository is controlled by usage of a fuzzy-based clustering technique. The best expected compromise solution stored in the repository is selected via the niching mechanism in a way that solutions are encouraged to seek the lesser explored regions. The proposed framework is applied in a typical grid-connected micro grid in order to verify its efficiency and feasibility.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.apenergy.2012.04.017</doi><tpages>16</tpages></addata></record> |
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| ispartof | Applied energy, 2012-11, Vol.99, p.455-470 |
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| language | eng |
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| source | Elsevier |
| subjects | algorithms Applied sciences Energy Exact sciences and technology Improved teaching–learning-based algorithm market prices Micro grid Multi-objective stochastic optimization optimization methods probability distribution Renewable energy management Self adaptive probabilistic modification strategy solutions stochastic models stochastic processes storing system optimization Uncertainty wind power |
| title | An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation |
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