Risk averse optimal operation of a virtual power plant using two stage stochastic programming
VPP (Virtual Power Plant) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The op...
Gespeichert in:
| Veröffentlicht in: | Energy (Oxford) 2014-08, Vol.73, p.958-967 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 967 |
|---|---|
| container_issue | |
| container_start_page | 958 |
| container_title | Energy (Oxford) |
| container_volume | 73 |
| creator | Tajeddini, Mohammad Amin Rahimi-Kian, Ashkan Soroudi, Alireza |
| description | VPP (Virtual Power Plant) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a GenCo (generation companies) expected profit. Furthermore, the CVaR (Conditional Value at Risk) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed.
•Virtual power plant modelling considering a set of energy generating and conversion units.•Uncertainty modelling using two stage stochastic programming technique.•Risk modelling using conditional value at risk.•Flexible operation of renewable energy resources.•Electricity price uncertainty in day ahead energy markets. |
| doi_str_mv | 10.1016/j.energy.2014.06.110 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1642268015</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0360544214008093</els_id><sourcerecordid>1642268015</sourcerecordid><originalsourceid>FETCH-LOGICAL-c514t-4c5985408cfa073f091ab02a14dd4556f786834b944b21fcd34f6075d19894813</originalsourceid><addsrcrecordid>eNqNkMFqGzEQhnVIoamTN-hBl0Iv3o60kla6FEpo00IgUJpjEbJ25Mhdr7aS7JC3r4xDj6WXGRi-mX_4CHnLoGPA1IddhzPm7XPHgYkOVMcYXJBL6BWspRD8NXlTyg4ApDbmkvz8Hssv6o6YC9K01Lh3U-uYXY1ppilQR48x10MbL-kJM10mN1d6KHHe0vqUaKlui60m_-hKjZ4uOW2z2-8bcEVeBTcVvH7pK_Lw5fOPm6_ru_vbbzef7tZeMlHXwkujpQDtg4OhD2CY2wB3TIyjkFKFQSvdi40RYsNZ8GMvgoJBjsxoIzTrV-T9-W7L_n3AUu0-Fo9TexXToVimBOdKA5P_gXIujekH01BxRn1OpWQMdsnNT362DOxJtt3Zs2x7km1B2Sa7rb17SXDFuylkN_tY_u5yPTA4JazIxzOHzcwxYrbFR5w9jjGjr3ZM8d9BfwBOvpjn</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1622599379</pqid></control><display><type>article</type><title>Risk averse optimal operation of a virtual power plant using two stage stochastic programming</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Tajeddini, Mohammad Amin ; Rahimi-Kian, Ashkan ; Soroudi, Alireza</creator><creatorcontrib>Tajeddini, Mohammad Amin ; Rahimi-Kian, Ashkan ; Soroudi, Alireza</creatorcontrib><description>VPP (Virtual Power Plant) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a GenCo (generation companies) expected profit. Furthermore, the CVaR (Conditional Value at Risk) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed.
•Virtual power plant modelling considering a set of energy generating and conversion units.•Uncertainty modelling using two stage stochastic programming technique.•Risk modelling using conditional value at risk.•Flexible operation of renewable energy resources.•Electricity price uncertainty in day ahead energy markets.</description><identifier>ISSN: 0360-5442</identifier><identifier>DOI: 10.1016/j.energy.2014.06.110</identifier><identifier>CODEN: ENEYDS</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Applied sciences ; CVaR ; Electric power generation ; Electric power plants ; Energy ; Exact sciences and technology ; Markets ; Mathematical models ; Mixed integer ; Optimization ; Risk ; Scenario based modelling ; Stochasticity ; Two-stage stochastic programming ; Uncertainty ; VPP</subject><ispartof>Energy (Oxford), 2014-08, Vol.73, p.958-967</ispartof><rights>2014 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c514t-4c5985408cfa073f091ab02a14dd4556f786834b944b21fcd34f6075d19894813</citedby><cites>FETCH-LOGICAL-c514t-4c5985408cfa073f091ab02a14dd4556f786834b944b21fcd34f6075d19894813</cites><orcidid>0000-0002-3651-6290</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.energy.2014.06.110$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28710599$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Tajeddini, Mohammad Amin</creatorcontrib><creatorcontrib>Rahimi-Kian, Ashkan</creatorcontrib><creatorcontrib>Soroudi, Alireza</creatorcontrib><title>Risk averse optimal operation of a virtual power plant using two stage stochastic programming</title><title>Energy (Oxford)</title><description>VPP (Virtual Power Plant) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a GenCo (generation companies) expected profit. Furthermore, the CVaR (Conditional Value at Risk) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed.
•Virtual power plant modelling considering a set of energy generating and conversion units.•Uncertainty modelling using two stage stochastic programming technique.•Risk modelling using conditional value at risk.•Flexible operation of renewable energy resources.•Electricity price uncertainty in day ahead energy markets.</description><subject>Applied sciences</subject><subject>CVaR</subject><subject>Electric power generation</subject><subject>Electric power plants</subject><subject>Energy</subject><subject>Exact sciences and technology</subject><subject>Markets</subject><subject>Mathematical models</subject><subject>Mixed integer</subject><subject>Optimization</subject><subject>Risk</subject><subject>Scenario based modelling</subject><subject>Stochasticity</subject><subject>Two-stage stochastic programming</subject><subject>Uncertainty</subject><subject>VPP</subject><issn>0360-5442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkMFqGzEQhnVIoamTN-hBl0Iv3o60kla6FEpo00IgUJpjEbJ25Mhdr7aS7JC3r4xDj6WXGRi-mX_4CHnLoGPA1IddhzPm7XPHgYkOVMcYXJBL6BWspRD8NXlTyg4ApDbmkvz8Hssv6o6YC9K01Lh3U-uYXY1ppilQR48x10MbL-kJM10mN1d6KHHe0vqUaKlui60m_-hKjZ4uOW2z2-8bcEVeBTcVvH7pK_Lw5fOPm6_ru_vbbzef7tZeMlHXwkujpQDtg4OhD2CY2wB3TIyjkFKFQSvdi40RYsNZ8GMvgoJBjsxoIzTrV-T9-W7L_n3AUu0-Fo9TexXToVimBOdKA5P_gXIujekH01BxRn1OpWQMdsnNT362DOxJtt3Zs2x7km1B2Sa7rb17SXDFuylkN_tY_u5yPTA4JazIxzOHzcwxYrbFR5w9jjGjr3ZM8d9BfwBOvpjn</recordid><startdate>20140814</startdate><enddate>20140814</enddate><creator>Tajeddini, Mohammad Amin</creator><creator>Rahimi-Kian, Ashkan</creator><creator>Soroudi, Alireza</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7U1</scope><scope>7U2</scope><scope>7U6</scope><scope>C1K</scope><scope>SOI</scope><scope>7SP</scope><scope>7SU</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-3651-6290</orcidid></search><sort><creationdate>20140814</creationdate><title>Risk averse optimal operation of a virtual power plant using two stage stochastic programming</title><author>Tajeddini, Mohammad Amin ; Rahimi-Kian, Ashkan ; Soroudi, Alireza</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c514t-4c5985408cfa073f091ab02a14dd4556f786834b944b21fcd34f6075d19894813</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>CVaR</topic><topic>Electric power generation</topic><topic>Electric power plants</topic><topic>Energy</topic><topic>Exact sciences and technology</topic><topic>Markets</topic><topic>Mathematical models</topic><topic>Mixed integer</topic><topic>Optimization</topic><topic>Risk</topic><topic>Scenario based modelling</topic><topic>Stochasticity</topic><topic>Two-stage stochastic programming</topic><topic>Uncertainty</topic><topic>VPP</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tajeddini, Mohammad Amin</creatorcontrib><creatorcontrib>Rahimi-Kian, Ashkan</creatorcontrib><creatorcontrib>Soroudi, Alireza</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</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><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tajeddini, Mohammad Amin</au><au>Rahimi-Kian, Ashkan</au><au>Soroudi, Alireza</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Risk averse optimal operation of a virtual power plant using two stage stochastic programming</atitle><jtitle>Energy (Oxford)</jtitle><date>2014-08-14</date><risdate>2014</risdate><volume>73</volume><spage>958</spage><epage>967</epage><pages>958-967</pages><issn>0360-5442</issn><coden>ENEYDS</coden><abstract>VPP (Virtual Power Plant) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a GenCo (generation companies) expected profit. Furthermore, the CVaR (Conditional Value at Risk) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed.
•Virtual power plant modelling considering a set of energy generating and conversion units.•Uncertainty modelling using two stage stochastic programming technique.•Risk modelling using conditional value at risk.•Flexible operation of renewable energy resources.•Electricity price uncertainty in day ahead energy markets.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2014.06.110</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-3651-6290</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0360-5442 |
| ispartof | Energy (Oxford), 2014-08, Vol.73, p.958-967 |
| issn | 0360-5442 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1642268015 |
| source | Elsevier ScienceDirect Journals Complete |
| subjects | Applied sciences CVaR Electric power generation Electric power plants Energy Exact sciences and technology Markets Mathematical models Mixed integer Optimization Risk Scenario based modelling Stochasticity Two-stage stochastic programming Uncertainty VPP |
| title | Risk averse optimal operation of a virtual power plant using two stage stochastic programming |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T06%3A35%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Risk%20averse%20optimal%20operation%20of%20a%20virtual%20power%20plant%20using%20two%20stage%20stochastic%20programming&rft.jtitle=Energy%20(Oxford)&rft.au=Tajeddini,%20Mohammad%20Amin&rft.date=2014-08-14&rft.volume=73&rft.spage=958&rft.epage=967&rft.pages=958-967&rft.issn=0360-5442&rft.coden=ENEYDS&rft_id=info:doi/10.1016/j.energy.2014.06.110&rft_dat=%3Cproquest_cross%3E1642268015%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1622599379&rft_id=info:pmid/&rft_els_id=S0360544214008093&rfr_iscdi=true |