Probabilistic Optimal Power Flow in Electricity Markets Based on a Two-Point Estimate Method
This paper presents an application of a two-point estimate method (2PEM) to account for uncertainties in the optimal power flow (OPF) problem in the context of competitive electricity markets. These uncertainties can be seen as a by-product of the economic pressure that forces market participants to...
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Veröffentlicht in: | IEEE transactions on power systems 2006-11, Vol.21 (4), p.1883-1893 |
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description | This paper presents an application of a two-point estimate method (2PEM) to account for uncertainties in the optimal power flow (OPF) problem in the context of competitive electricity markets. These uncertainties can be seen as a by-product of the economic pressure that forces market participants to behave in an "unpredictable" manner; hence, probability distributions of locational marginal prices are calculated as a result. Instead of using computationally demanding methods, the proposed approach needs 2n runs of the deterministic OPF for n uncertain variables to get the result in terms of the first three moments of the corresponding probability density functions. Another advantage of the 2PEM is that it does not require derivatives of the nonlinear function used in the computation of the probability distributions. The proposed method is tested on a simple three-bus test system and on a more realistic 129-bus test system. Results are compared against more accurate results obtained from MCS. The proposed method demonstrates a high level of accuracy for mean values when compared to the MCS; for standard deviations, the results are better in those cases when the number of uncertain variables is relatively low or when their dispersion is not large |
doi_str_mv | 10.1109/TPWRS.2006.881146 |
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These uncertainties can be seen as a by-product of the economic pressure that forces market participants to behave in an "unpredictable" manner; hence, probability distributions of locational marginal prices are calculated as a result. Instead of using computationally demanding methods, the proposed approach needs 2n runs of the deterministic OPF for n uncertain variables to get the result in terms of the first three moments of the corresponding probability density functions. Another advantage of the 2PEM is that it does not require derivatives of the nonlinear function used in the computation of the probability distributions. The proposed method is tested on a simple three-bus test system and on a more realistic 129-bus test system. Results are compared against more accurate results obtained from MCS. The proposed method demonstrates a high level of accuracy for mean values when compared to the MCS; for standard deviations, the results are better in those cases when the number of uncertain variables is relatively low or when their dispersion is not large</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/TPWRS.2006.881146</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Byproducts ; Computation ; Electric utilities ; Electricity ; Electricity markets ; Electricity supply industry ; Estimates ; Load flow ; Markets ; Mathematical analysis ; Mathematical models ; Power markets ; Power system planning ; Power system stability ; probabilistic optimal power flow (OPF) ; Probability distribution ; Random variables ; Studies ; System testing ; Taylor series ; two-point estimate method (2PEM) ; Uncertainty</subject><ispartof>IEEE transactions on power systems, 2006-11, Vol.21 (4), p.1883-1893</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c324t-62b8a808f655a8c01670d2647d922a36eb8ed73128b16dc2f3e3548201dfeda83</citedby><cites>FETCH-LOGICAL-c324t-62b8a808f655a8c01670d2647d922a36eb8ed73128b16dc2f3e3548201dfeda83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1717593$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1717593$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Verbic, G.</creatorcontrib><creatorcontrib>Canizares, C.A.</creatorcontrib><title>Probabilistic Optimal Power Flow in Electricity Markets Based on a Two-Point Estimate Method</title><title>IEEE transactions on power systems</title><addtitle>TPWRS</addtitle><description>This paper presents an application of a two-point estimate method (2PEM) to account for uncertainties in the optimal power flow (OPF) problem in the context of competitive electricity markets. These uncertainties can be seen as a by-product of the economic pressure that forces market participants to behave in an "unpredictable" manner; hence, probability distributions of locational marginal prices are calculated as a result. Instead of using computationally demanding methods, the proposed approach needs 2n runs of the deterministic OPF for n uncertain variables to get the result in terms of the first three moments of the corresponding probability density functions. Another advantage of the 2PEM is that it does not require derivatives of the nonlinear function used in the computation of the probability distributions. The proposed method is tested on a simple three-bus test system and on a more realistic 129-bus test system. Results are compared against more accurate results obtained from MCS. The proposed method demonstrates a high level of accuracy for mean values when compared to the MCS; for standard deviations, the results are better in those cases when the number of uncertain variables is relatively low or when their dispersion is not large</description><subject>Byproducts</subject><subject>Computation</subject><subject>Electric utilities</subject><subject>Electricity</subject><subject>Electricity markets</subject><subject>Electricity supply industry</subject><subject>Estimates</subject><subject>Load flow</subject><subject>Markets</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Power markets</subject><subject>Power system planning</subject><subject>Power system stability</subject><subject>probabilistic optimal power flow (OPF)</subject><subject>Probability distribution</subject><subject>Random variables</subject><subject>Studies</subject><subject>System testing</subject><subject>Taylor series</subject><subject>two-point estimate method (2PEM)</subject><subject>Uncertainty</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LxDAQhoMouH78APESvHjqmo8mnR5VdlVQXHTFixDSdorR2qxJlsV_b9cVBE9zmOd9mXkIOeJszDkrz-az54fHsWBMjwE4z_UWGXGlIGO6KLfJiAGoDErFdslejG9sAIfFiLzMgq9s5ToXk6vp_SK5D9vRmV9hoNPOr6jr6aTDOgVXu_RF72x4xxTphY3YUN9TS-crn8286xOdxHU8Ib3D9OqbA7LT2i7i4e_cJ0_TyfzyOru9v7q5PL_NainylGlRgQUGrVbKQs24LlgjdF40pRBWaqwAm0JyARXXTS1aiVLlIBhvWmwsyH1yuuldBP-5xJjMh4s1dp3t0S-jgVJzXRayHMiTf-SbX4Z-OM6AViALkcsB4huoDj7GgK1ZhOGt8GU4M2vb5se2Wds2G9tD5niTcYj4xxe8UKWU3-Dveto</recordid><startdate>20061101</startdate><enddate>20061101</enddate><creator>Verbic, G.</creator><creator>Canizares, C.A.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>F28</scope></search><sort><creationdate>20061101</creationdate><title>Probabilistic Optimal Power Flow in Electricity Markets Based on a Two-Point Estimate Method</title><author>Verbic, G. ; Canizares, C.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c324t-62b8a808f655a8c01670d2647d922a36eb8ed73128b16dc2f3e3548201dfeda83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Byproducts</topic><topic>Computation</topic><topic>Electric utilities</topic><topic>Electricity</topic><topic>Electricity markets</topic><topic>Electricity supply industry</topic><topic>Estimates</topic><topic>Load flow</topic><topic>Markets</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Power markets</topic><topic>Power system planning</topic><topic>Power system stability</topic><topic>probabilistic optimal power flow (OPF)</topic><topic>Probability distribution</topic><topic>Random variables</topic><topic>Studies</topic><topic>System testing</topic><topic>Taylor series</topic><topic>two-point estimate method (2PEM)</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Verbic, G.</creatorcontrib><creatorcontrib>Canizares, C.A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on power systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Verbic, G.</au><au>Canizares, C.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Probabilistic Optimal Power Flow in Electricity Markets Based on a Two-Point Estimate Method</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>2006-11-01</date><risdate>2006</risdate><volume>21</volume><issue>4</issue><spage>1883</spage><epage>1893</epage><pages>1883-1893</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>This paper presents an application of a two-point estimate method (2PEM) to account for uncertainties in the optimal power flow (OPF) problem in the context of competitive electricity markets. These uncertainties can be seen as a by-product of the economic pressure that forces market participants to behave in an "unpredictable" manner; hence, probability distributions of locational marginal prices are calculated as a result. Instead of using computationally demanding methods, the proposed approach needs 2n runs of the deterministic OPF for n uncertain variables to get the result in terms of the first three moments of the corresponding probability density functions. Another advantage of the 2PEM is that it does not require derivatives of the nonlinear function used in the computation of the probability distributions. The proposed method is tested on a simple three-bus test system and on a more realistic 129-bus test system. Results are compared against more accurate results obtained from MCS. The proposed method demonstrates a high level of accuracy for mean values when compared to the MCS; for standard deviations, the results are better in those cases when the number of uncertain variables is relatively low or when their dispersion is not large</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2006.881146</doi><tpages>11</tpages></addata></record> |
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subjects | Byproducts Computation Electric utilities Electricity Electricity markets Electricity supply industry Estimates Load flow Markets Mathematical analysis Mathematical models Power markets Power system planning Power system stability probabilistic optimal power flow (OPF) Probability distribution Random variables Studies System testing Taylor series two-point estimate method (2PEM) Uncertainty |
title | Probabilistic Optimal Power Flow in Electricity Markets Based on a Two-Point Estimate Method |
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