Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services
Vehicle-to-grid (V2G), the provision of energy and ancillary services from an electric vehicle (EV) to the grid, has the potential to offer financial benefits to EV owners and system benefits to utilities. In this work a V2G algorithm is developed to optimize energy and ancillary services scheduling...
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Veröffentlicht in: | IEEE transactions on smart grid 2012-03, Vol.3 (1), p.351-359 |
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description | Vehicle-to-grid (V2G), the provision of energy and ancillary services from an electric vehicle (EV) to the grid, has the potential to offer financial benefits to EV owners and system benefits to utilities. In this work a V2G algorithm is developed to optimize energy and ancillary services scheduling. The ancillary services considered are load regulation and spinning reserves. The algorithm is developed to be used by an aggregator, which may be a utility or a third party. This algorithm maximizes profits to the aggregator while providing additional system flexibility and peak load shaving to the utility and low costs of EV charging to the customer. The formulation also takes into account unplanned EV departures during the contract periods and compensates accordingly. Simulations using a hypothetical group of 10 000 commuter EVs in the ERCOT system using different battery replacement costs demonstrate these significant benefits. |
doi_str_mv | 10.1109/TSG.2011.2164099 |
format | Article |
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A.</creatorcontrib><title>Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services</title><title>IEEE transactions on smart grid</title><addtitle>TSG</addtitle><description>Vehicle-to-grid (V2G), the provision of energy and ancillary services from an electric vehicle (EV) to the grid, has the potential to offer financial benefits to EV owners and system benefits to utilities. In this work a V2G algorithm is developed to optimize energy and ancillary services scheduling. The ancillary services considered are load regulation and spinning reserves. The algorithm is developed to be used by an aggregator, which may be a utility or a third party. This algorithm maximizes profits to the aggregator while providing additional system flexibility and peak load shaving to the utility and low costs of EV charging to the customer. The formulation also takes into account unplanned EV departures during the contract periods and compensates accordingly. Simulations using a hypothetical group of 10 000 commuter EVs in the ERCOT system using different battery replacement costs demonstrate these significant benefits.</description><subject>Aggregator</subject><subject>Batteries</subject><subject>Degradation</subject><subject>demand response</subject><subject>Discharges</subject><subject>Optimal scheduling</subject><subject>profit optimization</subject><subject>regulation</subject><subject>Spinning</subject><subject>spinning reserves</subject><subject>System-on-a-chip</subject><subject>vehicle-to-grid (V2G)</subject><issn>1949-3053</issn><issn>1949-3061</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kLFqwzAQhkVpoSHNXuiiF7B7kmzZGoNJ3UIgg9OuRpZOiYprB9kt-O3rkJB_uX-47zg-Qp4ZxIyBet1XZcyBsZgzmYBSd2TBVKIiAZLd33oqHslqGL5hjhBCcrUgxe40-h_d0soc0f62vjvQ3tEvPHrTYjT2URm8pZsOw2GiurN03RnftjpMtMLw5w0OT-TB6XbA1XUuyefbZl-8R9td-VGst5ERmRyjRORgpeDO6JSnnDuUqFLQDSS5lQ1nmVScNxrBukzrTEJuc5eluVWJRIFiSeBy14R-GAK6-hTm38NUM6jPHurZQ332UF89zMjLBfGIeFuXwJlIc_EPHiJYSg</recordid><startdate>201203</startdate><enddate>201203</enddate><creator>Sortomme, E.</creator><creator>El-Sharkawi, M. 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A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-4380d632fca52522fe6e950ab048d6b2176922bae0df7aa7608d8f758d946e3e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Aggregator</topic><topic>Batteries</topic><topic>Degradation</topic><topic>demand response</topic><topic>Discharges</topic><topic>Optimal scheduling</topic><topic>profit optimization</topic><topic>regulation</topic><topic>Spinning</topic><topic>spinning reserves</topic><topic>System-on-a-chip</topic><topic>vehicle-to-grid (V2G)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sortomme, E.</creatorcontrib><creatorcontrib>El-Sharkawi, M. 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><jtitle>IEEE transactions on smart grid</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sortomme, E.</au><au>El-Sharkawi, M. A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services</atitle><jtitle>IEEE transactions on smart grid</jtitle><stitle>TSG</stitle><date>2012-03</date><risdate>2012</risdate><volume>3</volume><issue>1</issue><spage>351</spage><epage>359</epage><pages>351-359</pages><issn>1949-3053</issn><eissn>1949-3061</eissn><coden>ITSGBQ</coden><abstract>Vehicle-to-grid (V2G), the provision of energy and ancillary services from an electric vehicle (EV) to the grid, has the potential to offer financial benefits to EV owners and system benefits to utilities. In this work a V2G algorithm is developed to optimize energy and ancillary services scheduling. The ancillary services considered are load regulation and spinning reserves. The algorithm is developed to be used by an aggregator, which may be a utility or a third party. This algorithm maximizes profits to the aggregator while providing additional system flexibility and peak load shaving to the utility and low costs of EV charging to the customer. The formulation also takes into account unplanned EV departures during the contract periods and compensates accordingly. Simulations using a hypothetical group of 10 000 commuter EVs in the ERCOT system using different battery replacement costs demonstrate these significant benefits.</abstract><pub>IEEE</pub><doi>10.1109/TSG.2011.2164099</doi><tpages>9</tpages></addata></record> |
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subjects | Aggregator Batteries Degradation demand response Discharges Optimal scheduling profit optimization regulation Spinning spinning reserves System-on-a-chip vehicle-to-grid (V2G) |
title | Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services |
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