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Variability of the value of Vehicle-to-grid across vehicle and time in future California grid

dc.contributor.authorWang, Meiye
dc.contributor.advisorCraig, Michael
dc.date.accessioned2021-05-04T12:36:28Z
dc.date.issued2021-04
dc.date.submitted2021-04
dc.identifier.urihttps://hdl.handle.net/2027.42/167344
dc.description.abstractElectric vehicles (EVs) are gaining momentum across the globe as a strategy to combat climate change, however, uncontrolled charging of EVs can create pressure on electricity grid. Along with smart charging (V1G), Vehicle-to-grid (V2G) technology presents an opportunity for a new way of vehicle grid integration that enables EVs to send electricity back to the grid, creating the potential for EVs to provide grid services including electricity generation as well as regulation up and regulation down capacity. This study aims to quantify the economic value of V2G in the 2025 and 2030 California grid using an EV simulation model and a grid Unit Commitment Economic Dispatch model. Scenarios on different renewable penetration and battery cost are included to account for uncertainty in future energy and battery development. Results show a V2G-enabled EVs can generate an average of $32-$48 more total annual net revenue than V1G, most profits come from EVs providing electricity and a small amount from regulation down capacity. From 2020 to 2030, the economic value of V1G and V2G increased, the result also shows a tradeoff exists between renewable deployment and V2G value. V2G can generate a moderate amount of economic benefit given access to electricity and ancillary service wholesale market, which need further policy support and third-party business cases.en_US
dc.language.isoen_USen_US
dc.subjectV2Gen_US
dc.subjectVGIen_US
dc.subjectrevenueen_US
dc.titleVariability of the value of Vehicle-to-grid across vehicle and time in future California griden_US
dc.typeThesisen_US
dc.description.thesisdegreenameMaster of Science (MS)en_US
dc.description.thesisdegreedisciplineSchool for Environment and Sustainabilityen_US
dc.description.thesisdegreegrantorUniversity of Michiganen_US
dc.contributor.committeememberGronewold, Andrew
dc.identifier.uniqnamemeiyeen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/167344/1/Wang_Meiye_thesis.pdf
dc.identifier.doihttps://dx.doi.org/10.7302/1019
dc.working.doi10.7302/1019en_US
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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