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Social welfare maximisation of market based wind integrated power systems by simultaneous coordination of transmission switching and demand response programs
The non-preventable ever-increasing rate of wind power generation in market-based power systems faces the operators with challenging situations for making optimal decisions. So, it is essential to equip the operators with applicable control strategies and further corresponding control facilities. Mo...
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Published in: | IET renewable power generation 2019-05, Vol.13 (7), p.1037-1049 |
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description | The non-preventable ever-increasing rate of wind power generation in market-based power systems faces the operators with challenging situations for making optimal decisions. So, it is essential to equip the operators with applicable control strategies and further corresponding control facilities. Moreover, the high-priority of cheap wind power utilisation increases the probability of transmission lines congestion. Therefore, different solutions such as transmission switching (TS) and demand response (DR) programs have been recently introduced to manage the intermittent wind power generations. Accordingly, this study addresses the social welfare maximisation problem with coordinated control of TS and DR facilities to handle the regarding uncertainties using yet another linear matrix inequality parser (YALMIP). In fact, rapid algorithm and powerful employed solvers as well as simplicity of use, make YALMIP a practical modelling and optimisation toolbox. In this respect, the MOSEK solver is preferred by YALMIP to solve the proposed mixed integer linear programming problem. In addition, wind power uncertainty is modelled using the discrete-time Markov chain approach and optimisations are performed on the 8-bus and the large-scale IEEE 118-bus test systems. Results show that the proposed control strategy is highly capable of maximising social welfare by determining the optimal control commands in a real-time manner. |
doi_str_mv | 10.1049/iet-rpg.2018.5295 |
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So, it is essential to equip the operators with applicable control strategies and further corresponding control facilities. Moreover, the high-priority of cheap wind power utilisation increases the probability of transmission lines congestion. Therefore, different solutions such as transmission switching (TS) and demand response (DR) programs have been recently introduced to manage the intermittent wind power generations. Accordingly, this study addresses the social welfare maximisation problem with coordinated control of TS and DR facilities to handle the regarding uncertainties using yet another linear matrix inequality parser (YALMIP). In fact, rapid algorithm and powerful employed solvers as well as simplicity of use, make YALMIP a practical modelling and optimisation toolbox. In this respect, the MOSEK solver is preferred by YALMIP to solve the proposed mixed integer linear programming problem. In addition, wind power uncertainty is modelled using the discrete-time Markov chain approach and optimisations are performed on the 8-bus and the large-scale IEEE 118-bus test systems. Results show that the proposed control strategy is highly capable of maximising social welfare by determining the optimal control commands in a real-time manner.</description><identifier>ISSN: 1752-1416</identifier><identifier>ISSN: 1752-1424</identifier><identifier>EISSN: 1752-1424</identifier><identifier>DOI: 10.1049/iet-rpg.2018.5295</identifier><language>eng</language><publisher>The Institution of Engineering and Technology</publisher><subject>applicable control strategies ; challenging situation ; cheap wind power utilisation ; control strategy ; coordinated control ; corresponding control facilities ; demand response programs ; demand side management ; different solutions ; DR facilities ; high‐priority ; integer programming ; intermittent wind power generations ; large‐scale IEEE 118‐bus test systems ; linear matrix inequalities ; linear matrix inequality parser ; linear programming ; market based wind integrated power systems ; market‐based power systems ; Markov processes ; mixed integer linear programming problem ; optimal control ; optimal control commands ; optimal decisions ; optimisation ; optimisation toolbox ; optimisations ; power generation economics ; power generation planning ; power generation reliability ; power generation scheduling ; power markets ; powerful employed solvers ; practical modelling ; probability ; Research Article ; simultaneous coordination ; social welfare maximisation problem ; transmission lines congestion ; transmission switching ; wind power generation ; wind power plants ; wind power uncertainty ; YALMIP</subject><ispartof>IET renewable power generation, 2019-05, Vol.13 (7), p.1037-1049</ispartof><rights>The Institution of Engineering and Technology</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3342-4b29286da265184e12aa01fcbd4124f638f1b6ed32146068aaf5edd66cc8d91e3</citedby><cites>FETCH-LOGICAL-c3342-4b29286da265184e12aa01fcbd4124f638f1b6ed32146068aaf5edd66cc8d91e3</cites><orcidid>0000-0001-5834-5552</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1049%2Fiet-rpg.2018.5295$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1049%2Fiet-rpg.2018.5295$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,11562,27924,27925,46052,46476</link.rule.ids><linktorsrc>$$Uhttps://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-rpg.2018.5295$$EView_record_in_Wiley-Blackwell$$FView_record_in_$$GWiley-Blackwell</linktorsrc></links><search><creatorcontrib>Arasteh, Farzad</creatorcontrib><creatorcontrib>Riahy, Gholam H</creatorcontrib><title>Social welfare maximisation of market based wind integrated power systems by simultaneous coordination of transmission switching and demand response programs</title><title>IET renewable power generation</title><description>The non-preventable ever-increasing rate of wind power generation in market-based power systems faces the operators with challenging situations for making optimal decisions. So, it is essential to equip the operators with applicable control strategies and further corresponding control facilities. Moreover, the high-priority of cheap wind power utilisation increases the probability of transmission lines congestion. Therefore, different solutions such as transmission switching (TS) and demand response (DR) programs have been recently introduced to manage the intermittent wind power generations. Accordingly, this study addresses the social welfare maximisation problem with coordinated control of TS and DR facilities to handle the regarding uncertainties using yet another linear matrix inequality parser (YALMIP). In fact, rapid algorithm and powerful employed solvers as well as simplicity of use, make YALMIP a practical modelling and optimisation toolbox. In this respect, the MOSEK solver is preferred by YALMIP to solve the proposed mixed integer linear programming problem. In addition, wind power uncertainty is modelled using the discrete-time Markov chain approach and optimisations are performed on the 8-bus and the large-scale IEEE 118-bus test systems. Results show that the proposed control strategy is highly capable of maximising social welfare by determining the optimal control commands in a real-time manner.</description><subject>applicable control strategies</subject><subject>challenging situation</subject><subject>cheap wind power utilisation</subject><subject>control strategy</subject><subject>coordinated control</subject><subject>corresponding control facilities</subject><subject>demand response programs</subject><subject>demand side management</subject><subject>different solutions</subject><subject>DR facilities</subject><subject>high‐priority</subject><subject>integer programming</subject><subject>intermittent wind power generations</subject><subject>large‐scale IEEE 118‐bus test systems</subject><subject>linear matrix inequalities</subject><subject>linear matrix inequality parser</subject><subject>linear programming</subject><subject>market based wind integrated power systems</subject><subject>market‐based power systems</subject><subject>Markov processes</subject><subject>mixed integer linear programming problem</subject><subject>optimal control</subject><subject>optimal control commands</subject><subject>optimal decisions</subject><subject>optimisation</subject><subject>optimisation toolbox</subject><subject>optimisations</subject><subject>power generation economics</subject><subject>power generation planning</subject><subject>power generation reliability</subject><subject>power generation scheduling</subject><subject>power markets</subject><subject>powerful employed solvers</subject><subject>practical modelling</subject><subject>probability</subject><subject>Research Article</subject><subject>simultaneous coordination</subject><subject>social welfare maximisation problem</subject><subject>transmission lines congestion</subject><subject>transmission switching</subject><subject>wind power generation</subject><subject>wind power plants</subject><subject>wind power uncertainty</subject><subject>YALMIP</subject><issn>1752-1416</issn><issn>1752-1424</issn><issn>1752-1424</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqFkMtOwzAQRSMEEs8PYOcfSLEdxyTsoKIFCQnEYx058aS4JHbkcRX6MfwrjopYwmp8JZ87mpMk54zOGBXlhYGQ-mE145QVs5yX-V5yxC5znjLBxf7vm8nD5BhxTWle0kIeJV8vrjGqIyN0rfJAevVpeoMqGGeJa2P2HxBIrRA0GY3VxNgAK69CzIMbwRPcYoAeSb0laPpNF5QFt0HSOOe1sb9VwSuLsRunjKMJzbuxK6Jip4Z-Gh5wcBaBDN7FFT2eJget6hDOfuZJ8ra4fZ3fpQ-Py_v59UPaZJngqah5yQupFZc5KwQwrhRlbVNrwbhoZVa0rJagM86EpLJQqs1BaymbptAlg-wkYbvexjtED201eBNP31aMVpPfKvqtot9q8ltNfiNztWNG08H2f6B6flrymwVlVPIIpzt4-rZ2G2_jeX8s-wbDYpa1</recordid><startdate>20190520</startdate><enddate>20190520</enddate><creator>Arasteh, Farzad</creator><creator>Riahy, Gholam H</creator><general>The Institution of Engineering and Technology</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-5834-5552</orcidid></search><sort><creationdate>20190520</creationdate><title>Social welfare maximisation of market based wind integrated power systems by simultaneous coordination of transmission switching and demand response programs</title><author>Arasteh, Farzad ; Riahy, Gholam H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3342-4b29286da265184e12aa01fcbd4124f638f1b6ed32146068aaf5edd66cc8d91e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>applicable control strategies</topic><topic>challenging situation</topic><topic>cheap wind power utilisation</topic><topic>control strategy</topic><topic>coordinated control</topic><topic>corresponding control facilities</topic><topic>demand response programs</topic><topic>demand side management</topic><topic>different solutions</topic><topic>DR facilities</topic><topic>high‐priority</topic><topic>integer programming</topic><topic>intermittent wind power generations</topic><topic>large‐scale IEEE 118‐bus test systems</topic><topic>linear matrix inequalities</topic><topic>linear matrix inequality parser</topic><topic>linear programming</topic><topic>market based wind integrated power systems</topic><topic>market‐based power systems</topic><topic>Markov processes</topic><topic>mixed integer linear programming problem</topic><topic>optimal control</topic><topic>optimal control commands</topic><topic>optimal decisions</topic><topic>optimisation</topic><topic>optimisation toolbox</topic><topic>optimisations</topic><topic>power generation economics</topic><topic>power generation planning</topic><topic>power generation reliability</topic><topic>power generation scheduling</topic><topic>power markets</topic><topic>powerful employed solvers</topic><topic>practical modelling</topic><topic>probability</topic><topic>Research Article</topic><topic>simultaneous coordination</topic><topic>social welfare maximisation problem</topic><topic>transmission lines congestion</topic><topic>transmission switching</topic><topic>wind power generation</topic><topic>wind power plants</topic><topic>wind power uncertainty</topic><topic>YALMIP</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arasteh, Farzad</creatorcontrib><creatorcontrib>Riahy, Gholam H</creatorcontrib><collection>CrossRef</collection><jtitle>IET renewable power generation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Arasteh, Farzad</au><au>Riahy, Gholam H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Social welfare maximisation of market based wind integrated power systems by simultaneous coordination of transmission switching and demand response programs</atitle><jtitle>IET renewable power generation</jtitle><date>2019-05-20</date><risdate>2019</risdate><volume>13</volume><issue>7</issue><spage>1037</spage><epage>1049</epage><pages>1037-1049</pages><issn>1752-1416</issn><issn>1752-1424</issn><eissn>1752-1424</eissn><abstract>The non-preventable ever-increasing rate of wind power generation in market-based power systems faces the operators with challenging situations for making optimal decisions. So, it is essential to equip the operators with applicable control strategies and further corresponding control facilities. Moreover, the high-priority of cheap wind power utilisation increases the probability of transmission lines congestion. Therefore, different solutions such as transmission switching (TS) and demand response (DR) programs have been recently introduced to manage the intermittent wind power generations. Accordingly, this study addresses the social welfare maximisation problem with coordinated control of TS and DR facilities to handle the regarding uncertainties using yet another linear matrix inequality parser (YALMIP). In fact, rapid algorithm and powerful employed solvers as well as simplicity of use, make YALMIP a practical modelling and optimisation toolbox. In this respect, the MOSEK solver is preferred by YALMIP to solve the proposed mixed integer linear programming problem. In addition, wind power uncertainty is modelled using the discrete-time Markov chain approach and optimisations are performed on the 8-bus and the large-scale IEEE 118-bus test systems. Results show that the proposed control strategy is highly capable of maximising social welfare by determining the optimal control commands in a real-time manner.</abstract><pub>The Institution of Engineering and Technology</pub><doi>10.1049/iet-rpg.2018.5295</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-5834-5552</orcidid></addata></record> |
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subjects | applicable control strategies challenging situation cheap wind power utilisation control strategy coordinated control corresponding control facilities demand response programs demand side management different solutions DR facilities high‐priority integer programming intermittent wind power generations large‐scale IEEE 118‐bus test systems linear matrix inequalities linear matrix inequality parser linear programming market based wind integrated power systems market‐based power systems Markov processes mixed integer linear programming problem optimal control optimal control commands optimal decisions optimisation optimisation toolbox optimisations power generation economics power generation planning power generation reliability power generation scheduling power markets powerful employed solvers practical modelling probability Research Article simultaneous coordination social welfare maximisation problem transmission lines congestion transmission switching wind power generation wind power plants wind power uncertainty YALMIP |
title | Social welfare maximisation of market based wind integrated power systems by simultaneous coordination of transmission switching and demand response programs |
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