A Kernel-Based Predictive Model of EV Capacity for Distributed Voltage Control and Demand Response
Energy storage and reactive power supplied by electric vehicles (EVs) through vehicle-to-grid (V2G) operation can be coordinated to provide voltage support, thus reducing the need of grid reinforcement and active power curtailment. Optimization and control approaches for V2G-enabled reactive power c...
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| Veröffentlicht in: | IEEE transactions on smart grid 2018-07, Vol.9 (4), p.3180-3190 |
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| container_title | IEEE transactions on smart grid |
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| creator | Valizadeh Haghi, Hamed Qu, Zhihua |
| description | Energy storage and reactive power supplied by electric vehicles (EVs) through vehicle-to-grid (V2G) operation can be coordinated to provide voltage support, thus reducing the need of grid reinforcement and active power curtailment. Optimization and control approaches for V2G-enabled reactive power control should be robust to variations and offer a certain level of optimality by combining real-time control with an hours-ahead scheduling scheme. This paper introduces an optimization and control framework that can be used for charging batteries and managing available storage while using the remaining capacity of the chargers to generate reactive power and cooperatively perform voltage control. Stochastic distributed optimization of reactive power is realized by integrating a robust distributed sub-gradient method with conditional ensemble predictions of V2G capacity. Hence, the proposed solutions can meet system operational requirements for the upcoming hours by enabling instantaneous cooperation among distributed EVs. |
| doi_str_mv | 10.1109/TSG.2016.2628367 |
| format | Article |
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Hence, the proposed solutions can meet system operational requirements for the upcoming hours by enabling instantaneous cooperation among distributed EVs.</description><subject>Batteries</subject><subject>Charging stations</subject><subject>Coordinated voltage control</subject><subject>distributed optimization</subject><subject>electric vehicles</subject><subject>Engineering</subject><subject>Optimization</subject><subject>Predictive models</subject><subject>Reactive power</subject><subject>stochastic process</subject><subject>Uncertainty</subject><subject>Voltage control</subject><issn>1949-3053</issn><issn>1949-3061</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhhdRsNTeBS_B-9Ykk_3IsW61ihVFS68hm53VyHZTkij037ulpTOHmcPzDsOTJNeMThmj8m71uZhyyvIpz3kJeXGWjJgUMgWas_PTnsFlMgnhhw4FADmXo6SekRf0PXbpvQ7YkHePjTXR_iF5dQ12xLXkYU0qvdXGxh1pnSdzG6K39W8c-LXrov5CUrk-etcR3Tdkjpv9-MCwdX3Aq-Si1V3AyXGOk9Xjw6p6Spdvi-dqtkwNlzSmhYCmzgoUjHKBBddaF1pwUYNknDW5YCYroUSeM2NkCTyDoTOKUgPoGsbJ7eGsC9GqMHyL5tu4vkcTFcsEyzIYIHqAjHcheGzV1tuN9jvFqNqrVINKtVepjiqHyM0hYhHxhBeF4BJK-AdiCG2I</recordid><startdate>201807</startdate><enddate>201807</enddate><creator>Valizadeh Haghi, Hamed</creator><creator>Qu, Zhihua</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>OTOTI</scope><orcidid>https://orcid.org/0000-0002-6039-4828</orcidid><orcidid>https://orcid.org/0000000260394828</orcidid></search><sort><creationdate>201807</creationdate><title>A Kernel-Based Predictive Model of EV Capacity for Distributed Voltage Control and Demand Response</title><author>Valizadeh Haghi, Hamed ; Qu, Zhihua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c290t-743db57e41024e72aaa7a424b39121d641c5838e261cc98325353550e9a33ab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Batteries</topic><topic>Charging stations</topic><topic>Coordinated voltage control</topic><topic>distributed optimization</topic><topic>electric vehicles</topic><topic>Engineering</topic><topic>Optimization</topic><topic>Predictive models</topic><topic>Reactive power</topic><topic>stochastic process</topic><topic>Uncertainty</topic><topic>Voltage control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Valizadeh Haghi, Hamed</creatorcontrib><creatorcontrib>Qu, Zhihua</creatorcontrib><creatorcontrib>Univ. of Central Florida, Orlando, FL (United States)</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>OSTI.GOV</collection><jtitle>IEEE transactions on smart grid</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Valizadeh Haghi, Hamed</au><au>Qu, Zhihua</au><aucorp>Univ. of Central Florida, Orlando, FL (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Kernel-Based Predictive Model of EV Capacity for Distributed Voltage Control and Demand Response</atitle><jtitle>IEEE transactions on smart grid</jtitle><stitle>TSG</stitle><date>2018-07</date><risdate>2018</risdate><volume>9</volume><issue>4</issue><spage>3180</spage><epage>3190</epage><pages>3180-3190</pages><issn>1949-3053</issn><eissn>1949-3061</eissn><coden>ITSGBQ</coden><abstract>Energy storage and reactive power supplied by electric vehicles (EVs) through vehicle-to-grid (V2G) operation can be coordinated to provide voltage support, thus reducing the need of grid reinforcement and active power curtailment. 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| ispartof | IEEE transactions on smart grid, 2018-07, Vol.9 (4), p.3180-3190 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Batteries Charging stations Coordinated voltage control distributed optimization electric vehicles Engineering Optimization Predictive models Reactive power stochastic process Uncertainty Voltage control |
| title | A Kernel-Based Predictive Model of EV Capacity for Distributed Voltage Control and Demand Response |
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