Two-Stage Stochastic Programming Model for Market Clearing With Contingencies
Planning for contingencies typically results in the use of more expensive facilities before disruptions. It leads to different prices and energy availability at various network locations depending on how the contingency analysis is performed. In this paper we present a two-stage stochastic programmi...
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Veröffentlicht in: | IEEE transactions on power systems 2009-08, Vol.24 (3), p.1266-1278 |
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creator | Saric, A.T. Murphy, F.H. Soyster, A.L. Stankovic, A.M. |
description | Planning for contingencies typically results in the use of more expensive facilities before disruptions. It leads to different prices and energy availability at various network locations depending on how the contingency analysis is performed. In this paper we present a two-stage stochastic programming model for incorporating contingencies. The model is computationally demanding, and made tractable by using an interior-point log-barrier method coupled with Benders decomposition. The second-stage optimal recourse function (RF) defines the most economically efficient actions in the post-contingency state for returning the system back to normal operating conditions. The approach is illustrated with for two examples: small (with 8 buses/11 branches) and IEEE medium-scale (with 300 buses/411 branches). |
doi_str_mv | 10.1109/TPWRS.2009.2023267 |
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(IEEE) 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-3e81e86e1a7d2cd75de8b8637a10e6946d9c4dd45d25b6e219bbd71d3baea85e3</citedby><cites>FETCH-LOGICAL-c358t-3e81e86e1a7d2cd75de8b8637a10e6946d9c4dd45d25b6e219bbd71d3baea85e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5161353$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27922,27923,54756</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5161353$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Saric, A.T.</creatorcontrib><creatorcontrib>Murphy, F.H.</creatorcontrib><creatorcontrib>Soyster, A.L.</creatorcontrib><creatorcontrib>Stankovic, A.M.</creatorcontrib><title>Two-Stage Stochastic Programming Model for Market Clearing With Contingencies</title><title>IEEE transactions on power systems</title><addtitle>TPWRS</addtitle><description>Planning for contingencies typically results in the use of more expensive facilities before disruptions. It leads to different prices and energy availability at various network locations depending on how the contingency analysis is performed. In this paper we present a two-stage stochastic programming model for incorporating contingencies. The model is computationally demanding, and made tractable by using an interior-point log-barrier method coupled with Benders decomposition. The second-stage optimal recourse function (RF) defines the most economically efficient actions in the post-contingency state for returning the system back to normal operating conditions. The approach is illustrated with for two examples: small (with 8 buses/11 branches) and IEEE medium-scale (with 300 buses/411 branches).</description><subject>Availability</subject><subject>Buses (vehicles)</subject><subject>Computational modeling</subject><subject>Contingency</subject><subject>Linear matrix inequalities</subject><subject>Locational marginal prices (LMPs)</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Optimization</subject><subject>optimization methods</subject><subject>Performance analysis</subject><subject>Power generation economics</subject><subject>Power system economics</subject><subject>Power system modeling</subject><subject>power system security</subject><subject>Programming</subject><subject>Radio frequency</subject><subject>stochastic approximation</subject><subject>Stochastic processes</subject><subject>Stochasticity</subject><subject>Transmission line matrix methods</subject><subject>uncertainty</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kU1Lw0AQhhdRsFb_gF6CB_GSuh_Zr6MEv6DFYis9LpvstE1Ns3U3Rfz3plY8ePAywzDPOzA8CJ0TPCAE65vpePYyGVCMdVcoo0IeoB7hXKVYSH2IelgpnirN8TE6iXGFMRbdoodG0w-fTlq7gGTS-nJpY1uVyTj4RbDrddUskpF3UCdzH5KRDW_QJnkNNuw2s6pdJrlv2m6ApqwgnqKjua0jnP30Pnq9v5vmj-nw-eEpvx2mJeOqTRkoAkoAsdLR0knuQBVKMGkJBqEz4XSZOZdxR3khgBJdFE4SxwoLVnFgfXS1v7sJ_n0LsTXrKpZQ17YBv42GCSYyTmgHXv8LEiEJlSSTokMv_6Arvw1N94ZRXAnNlWYdRPdQGXyMAeZmE6q1DZ-GYLMzYb5NmJ0J82OiC13sQxUA_AY4EYRxxr4A3CmEzA</recordid><startdate>20090801</startdate><enddate>20090801</enddate><creator>Saric, A.T.</creator><creator>Murphy, F.H.</creator><creator>Soyster, A.L.</creator><creator>Stankovic, A.M.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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It leads to different prices and energy availability at various network locations depending on how the contingency analysis is performed. In this paper we present a two-stage stochastic programming model for incorporating contingencies. The model is computationally demanding, and made tractable by using an interior-point log-barrier method coupled with Benders decomposition. The second-stage optimal recourse function (RF) defines the most economically efficient actions in the post-contingency state for returning the system back to normal operating conditions. The approach is illustrated with for two examples: small (with 8 buses/11 branches) and IEEE medium-scale (with 300 buses/411 branches).</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2009.2023267</doi><tpages>13</tpages></addata></record> |
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subjects | Availability Buses (vehicles) Computational modeling Contingency Linear matrix inequalities Locational marginal prices (LMPs) Mathematical analysis Mathematical models Optimization optimization methods Performance analysis Power generation economics Power system economics Power system modeling power system security Programming Radio frequency stochastic approximation Stochastic processes Stochasticity Transmission line matrix methods uncertainty |
title | Two-Stage Stochastic Programming Model for Market Clearing With Contingencies |
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