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A human expert-based approach to electrical peak demand management
The objective of this paper is to propose a human expert-based approach to electrical peak demand management. The proposed approach helps to allocate demand curtailments (MW) among distribution substations (DS) or feeders in an electric utility service area based on requirements of the central load...
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creator | Bian, Desong Pipattanasomporn, Manisa Rahman, Saifur |
description | The objective of this paper is to propose a human expert-based approach to electrical peak demand management. The proposed approach helps to allocate demand curtailments (MW) among distribution substations (DS) or feeders in an electric utility service area based on requirements of the central load dispatch center. Demand curtailment allocation is quantified by taking into account demand response (DR) potential and load curtailment priority of each DS, which can be determined using DS loading level, capacity of each DS, customer types (residential/commercial), and load categories (deployable, interruptible, or critical). The analytic hierarchy process is used to model a complex decision-making process according to both expert inputs and objective parameters. Simulation case studies are conducted to demonstrate how the proposed approach can be implemented to perform DR using real-world data from an electric utility. Simulation results demonstrate that the proposed approach is capable of achieving realistic demand curtailment allocations among different DSs to meet the peak load reduction requirements at the utility level. |
doi_str_mv | 10.1109/PESGM.2015.7286112 |
format | conference_proceeding |
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The proposed approach helps to allocate demand curtailments (MW) among distribution substations (DS) or feeders in an electric utility service area based on requirements of the central load dispatch center. Demand curtailment allocation is quantified by taking into account demand response (DR) potential and load curtailment priority of each DS, which can be determined using DS loading level, capacity of each DS, customer types (residential/commercial), and load categories (deployable, interruptible, or critical). The analytic hierarchy process is used to model a complex decision-making process according to both expert inputs and objective parameters. Simulation case studies are conducted to demonstrate how the proposed approach can be implemented to perform DR using real-world data from an electric utility. 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The proposed approach helps to allocate demand curtailments (MW) among distribution substations (DS) or feeders in an electric utility service area based on requirements of the central load dispatch center. Demand curtailment allocation is quantified by taking into account demand response (DR) potential and load curtailment priority of each DS, which can be determined using DS loading level, capacity of each DS, customer types (residential/commercial), and load categories (deployable, interruptible, or critical). The analytic hierarchy process is used to model a complex decision-making process according to both expert inputs and objective parameters. Simulation case studies are conducted to demonstrate how the proposed approach can be implemented to perform DR using real-world data from an electric utility. Simulation results demonstrate that the proposed approach is capable of achieving realistic demand curtailment allocations among different DSs to meet the peak load reduction requirements at the utility level.</description><subject>Electric potential</subject><subject>Load management</subject><subject>Load modeling</subject><subject>Loading</subject><subject>Power industry</subject><subject>Resource management</subject><subject>Substations</subject><issn>1932-5517</issn><isbn>9781467380409</isbn><isbn>1467380407</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2015</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj9FKwzAUhiMoOOdeQG_yAq05SU6TXM4xpzBRUK_HaXriqu1W2gr69hu4m_-7-fjgF-IGVA6gwt3r8m31nGsFmDvtCwB9JmbBebCFM15ZFc7FBILRGSK4S3E1DF9KoQGrJ-J-Lrc_Le0k_3bcj1lJA1eSuq7fU9zKcS-54Tj2daRGdkzfsuKjXsnj0Ce3vBuvxUWiZuDZiVPx8bB8Xzxm65fV02K-zmowfsyQgkOmwqgYKTptEI0tAmikyLFM4KJFbZltAkyRIGkoK8_IyRqvk5mK2_9uzcybrq9b6v82p8vmAMsvStg</recordid><startdate>20150701</startdate><enddate>20150701</enddate><creator>Bian, Desong</creator><creator>Pipattanasomporn, Manisa</creator><creator>Rahman, Saifur</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20150701</creationdate><title>A human expert-based approach to electrical peak demand management</title><author>Bian, Desong ; Pipattanasomporn, Manisa ; Rahman, Saifur</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i138t-5a975ea630ccac723553469125acecbf17c4524ee4f15fca1f21bd8e5ef4382f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Electric potential</topic><topic>Load management</topic><topic>Load modeling</topic><topic>Loading</topic><topic>Power industry</topic><topic>Resource management</topic><topic>Substations</topic><toplevel>online_resources</toplevel><creatorcontrib>Bian, Desong</creatorcontrib><creatorcontrib>Pipattanasomporn, Manisa</creatorcontrib><creatorcontrib>Rahman, Saifur</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore (Online service)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bian, Desong</au><au>Pipattanasomporn, Manisa</au><au>Rahman, Saifur</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A human expert-based approach to electrical peak demand management</atitle><btitle>2015 IEEE Power & Energy Society General Meeting</btitle><stitle>PESGM</stitle><date>2015-07-01</date><risdate>2015</risdate><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>1932-5517</issn><eisbn>9781467380409</eisbn><eisbn>1467380407</eisbn><abstract>The objective of this paper is to propose a human expert-based approach to electrical peak demand management. The proposed approach helps to allocate demand curtailments (MW) among distribution substations (DS) or feeders in an electric utility service area based on requirements of the central load dispatch center. Demand curtailment allocation is quantified by taking into account demand response (DR) potential and load curtailment priority of each DS, which can be determined using DS loading level, capacity of each DS, customer types (residential/commercial), and load categories (deployable, interruptible, or critical). The analytic hierarchy process is used to model a complex decision-making process according to both expert inputs and objective parameters. Simulation case studies are conducted to demonstrate how the proposed approach can be implemented to perform DR using real-world data from an electric utility. Simulation results demonstrate that the proposed approach is capable of achieving realistic demand curtailment allocations among different DSs to meet the peak load reduction requirements at the utility level.</abstract><pub>IEEE</pub><doi>10.1109/PESGM.2015.7286112</doi><tpages>1</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Electric potential Load management Load modeling Loading Power industry Resource management Substations |
title | A human expert-based approach to electrical peak demand management |
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