Joint market clearing in a stochastic framework considering power system security
This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considere...
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Veröffentlicht in: | Applied energy 2009-09, Vol.86 (9), p.1675-1682 |
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description | This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte–Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method. |
doi_str_mv | 10.1016/j.apenergy.2009.01.021 |
format | Article |
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The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte–Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. 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The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte–Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method.</description><subject>Applied sciences</subject><subject>Economic data</subject><subject>Electric energy</subject><subject>Energy</subject><subject>Energy economics</subject><subject>Exact sciences and technology</subject><subject>Expected interruption cost</subject><subject>General, economic and professional studies</subject><subject>Lost opportunity cost</subject><subject>Market clearing</subject><subject>Market clearing Stochastic optimization Offer cost Expected interruption cost Lost opportunity cost</subject><subject>Methodology. Modelling</subject><subject>Offer cost</subject><subject>Stochastic optimization</subject><issn>0306-2619</issn><issn>1872-9118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNqFkE1v1DAQhiMEEkvhLyBf4JYwthPbuYEqaEErIaT2bHknk9bbfGF7W-Xf4-2WXnt4Zy7POxo9RfGRQ8WBqy_7yi00UbhZKwHQVsArEPxVseFGi7Ll3LwuNiBBlULx9m3xLsY9QEYEbIo_v2Y_JTa6cEeJ4UAu-OmG-Yk5FtOMty4mj6wPbqSHOdwxnKfoO3qklvmBAotrTDSySHgIPq3vize9GyJ9eNpnxfWP71fnl-X298XP82_bEmstUylBtvWu77RB3QhUCrTRjW6UhK4Rpu8koXaa77jAHfStdtxxkDV2UMte7-RZ8fl0dwnz3wPFZEcfkYbBTTQfopV1A0Jq8yIoQNcaZJNBdQIxzDEG6u0SfDazWg72qNru7X_V9qjaArfZYy5uT8VAC-Fzi4jccuTtvZXOqDzWnMemdD7nuJccrnRjuTLC3qYxn_v09LCL6IbsfkIfn88KXhvVapm5ryeOsuZ7T8FG9DQhdT4QJtvN_qXP_wFGy7Hn</recordid><startdate>20090901</startdate><enddate>20090901</enddate><creator>Aghaei, J.</creator><creator>Shayanfar, H.A.</creator><creator>Amjady, N.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7TA</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20090901</creationdate><title>Joint market clearing in a stochastic framework considering power system security</title><author>Aghaei, J. ; Shayanfar, H.A. ; Amjady, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c473t-30394bfd78c752c660787575630d528fd3ec7a71b12cb0f97a1a1034cd043f7b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Applied sciences</topic><topic>Economic data</topic><topic>Electric energy</topic><topic>Energy</topic><topic>Energy economics</topic><topic>Exact sciences and technology</topic><topic>Expected interruption cost</topic><topic>General, economic and professional studies</topic><topic>Lost opportunity cost</topic><topic>Market clearing</topic><topic>Market clearing Stochastic optimization Offer cost Expected interruption cost Lost opportunity cost</topic><topic>Methodology. Modelling</topic><topic>Offer cost</topic><topic>Stochastic optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aghaei, J.</creatorcontrib><creatorcontrib>Shayanfar, H.A.</creatorcontrib><creatorcontrib>Amjady, N.</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Materials Business File</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Applied energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aghaei, J.</au><au>Shayanfar, H.A.</au><au>Amjady, N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint market clearing in a stochastic framework considering power system security</atitle><jtitle>Applied energy</jtitle><date>2009-09-01</date><risdate>2009</risdate><volume>86</volume><issue>9</issue><spage>1675</spage><epage>1682</epage><pages>1675-1682</pages><issn>0306-2619</issn><eissn>1872-9118</eissn><coden>APENDX</coden><abstract>This paper presents a new stochastic framework for provision of reserve requirements (spinning and non-spinning reserves) as well as energy in day-ahead simultaneous auctions by pool-based aggregated market scheme. The uncertainty of generating units in the form of system contingencies are considered in the market clearing procedure by the stochastic model. The solution methodology consists of two stages, which firstly, employs Monte–Carlo Simulation (MCS) for random scenario generation. Then, the stochastic market clearing procedure is implemented as a series of deterministic optimization problems (scenarios) including non-contingent scenario and different post-contingency states. The objective function of each of these deterministic optimization problems consists of offered cost function (including both energy and reserves offer costs), Lost Opportunity Cost (LOC) and Expected Interruption Cost (EIC). Each optimization problem is solved considering AC power flow and security constraints of the power system. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS) and simulation studies are carried out to examine the effectiveness of the proposed method.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.apenergy.2009.01.021</doi><tpages>8</tpages></addata></record> |
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subjects | Applied sciences Economic data Electric energy Energy Energy economics Exact sciences and technology Expected interruption cost General, economic and professional studies Lost opportunity cost Market clearing Market clearing Stochastic optimization Offer cost Expected interruption cost Lost opportunity cost Methodology. Modelling Offer cost Stochastic optimization |
title | Joint market clearing in a stochastic framework considering power system security |
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