Influence of the Electric vehicle battery size and EV penetration rate on the potential capacity of Vehicle-to-grid

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Applied CUE2018, 5–7 June 2018, Shanghai, China Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy Influence of thesystems, Electric vehicle battery size and EV penetration rate on the Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy procedia systems, CUE2018, 5–7 June 2018, Shanghai, China Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and potential capacitysystems, of Vehicle-to-grid CUE2018, 5–7 June 2018, Shanghai, China systems, CUE2018, 5–7 Junesize 2018,and Shanghai, China Influence ofurban the Electric vehicle5–7 battery EV penetration rate on the energy systems, June 2018, Shanghai, China Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy a a a a a a Influence ofLiuthe Electric size and penetration on the Yiling , Haiyang Lin ,vehicle Wang Yubattery , Liu Luyao , Qie SunEV , Ronald Wennerstenrate potential capacity of Vehicle-to-grid systems, CUE2018, 5–7 June 2018, Shanghai, China Influence of the Electric vehicle battery size and EV penetration rate a Influence ofofthe Electric vehicle batteryShandong size andUniversity, EV penetration rate on on the the Institute Thermal Science and Technology, China potential capacity of Vehicle-to-grid The 15th Symposium on District Heating and Cooling a International a a a a a potentialYiling capacity of Vehicle-to-grid Liu , Haiyang Lin , Wang Yu , Liu Luyao , Qie Sun , Ronald Wennersten

potential capacitya of Vehicle-to-grid a a a a and EV a penetration rate Influence of Liu theThermal Electric battery size Institute of Science and Technology, Shandong University, Yiling , Haiyang Linvehicle , Wang Yu , Liu Luyao , Qie Sun , RonaldChina Wennerstenaa on the a a a a a a Yiling Liua, Haiyang Lina, Wang Yua, Liu Luyaoa, Qie Suna, Ronald Wennerstena Abstract Institute ofthe Thermal Science and Technology, Shandong University, China Yiling Liu , of Haiyang Lin , Wang Yu ,using Liu Luyao , Qieheat Sun , Ronald Wennersten potential Vehicle-to-grid Assessing feasibility of the demand-outdoor a capacity Institute of Thermal Science and Technology, Shandong University, China a

Institute of Thermal Science and Technology, Shandong University, China

Abstract The idea that electric vehicles can connect toYu serve as energy storage energy devices Wennersten is compelling, especially Yilingfunction Liu , Haiyang Lin , Liu Luyaodistrict , Qie Sun , Ronald temperature for,toWang agridlong-term heat demand forecast in situations where traditional forms of storage, back-up energy supply is unavailable or expensive, or considering a

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Abstract a Institute of Thermal Science and Technology, Shandong University, China Abstract the volatility frequency. So far, andtoareliability of vehicle-to-grid(V2G) has is become a veryespecially popular The idea thatofelectric can the connect to grid serve as energy storage energy devices Abstract a,b,c vehicles a viability b ccompelling, c I.idea Andrić *,vehicles A. Pina , ofP.focused Ferrão J.maximum Fournier .,isB. O. Le Corre in situations traditional forms storage, back-up energy supply unavailable or expensive, orhowever, considering research topic. Previous studies mainly potential ofLacarrière V2Gdevices capacity, which, is The thatwhere electric can connect to gridon to,the serve as energy storage energy is ,compelling, especially The idea that electric vehicles can connect to grid to as energy storage energy devices is compelling, especially unrealistic to traditional achieve. potential capacity of V2G relevant tois the size of batteries and the penetration the volatility of frequency. SoThe far, theofviability reliability of vehicle-to-grid(V2G) aorvery popular in situations where forms storage, back-up energy supply unavailable orhas expensive, considering The idea that electric vehicles can connect to gridand to -serve serve as is energy storage energy devices isbecome compelling, especially a usually IN+Abstract Center for Innovation, Technology and Policy Research Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal in situations where traditional forms of storage, back-up energy supply is unavailable or expensive, or considering rates of electric vehicle. This article developed an agent-based model to estimate the real amount of electricity thatis research topic. Previous studies mainly focused on the maximum potential of V2G capacity, which, however, the volatility of frequency. So far, the viability and reliability of vehicle-to-grid(V2G) has become a very popular b in situations where traditional forms & ofInnovation, storage, back-up energy supply is unavailable or France expensive, or considering Veolia Recherche 291 Avenue Dreyfous Daniel, 78520 Limay, the volatility of frequency. So far, the viability and reliability of vehicle-to-grid(V2G) has become a very popular usually unrealistic to vehicles achieve. The potential of -V2G isofrelevant the ofand batteries and the penetration vehicles discharge to thestudies grid. Three sizes theenergy penetration rate of 90% the penetration rate fromis c can research topic. mainly focused onunder the maximum potential ofsize V2G capacity, which, however, the volatility ofPrevious frequency. Socan far, the battery viability and reliability vehicle-to-grid(V2G) has become a very popular The idea that electric connect to capacity grid to serve as storage energy devices is compelling, especially Département Systèmes Énergétiques et Environnement IMT Atlantique, 4to rue Alfred Kastler, 44300 Nantes, France research topic. Previous studies mainly focused on the maximum V2G capacity, which, however, is 10% toof90% were investigated. The results show that theV2G increase ofpotential battery capacity ledexpensive, to anand increase of V2G rates electric vehicle. This article developed an agent-based to theofreal amount of the electricity that usually unrealistic to achieve. The potential capacity of ismodel relevant tounavailable theof size batteries penetration in situations where traditional forms of storage, back-up energy supply isestimate or or considering research topic. Previous studies mainly focused on the maximum potential of V2G capacity, which, however, is usually unrealistic to achieve. The potential capacity of V2G is relevant to the size of batteries and the penetration vehicles can discharge to the grid. Three battery sizes under the penetration rate of 90% and the penetration rate from potential capacity from 1650.2 kW to 1868.5 kW and that increase from 10% to 90% in penetration rate could rates of unrealistic electric vehicle. This developed an model to estimate theofreal ofathe electricity that usually to achieve. The potential capacity of V2G isof relevant to the size batteries and penetration the volatility of frequency. Soarticle far, the viability andagent-based reliability vehicle-to-grid(V2G) hasamount become very popular rates of electric vehicle. article developed an model estimate the amount of electricity that 10% to 90% were investigated. The results show that the ofto led to penetration an increase of V2G account V2G potential capacity change from 162.8 kW toincrease 1650.2 kW. vehicles can discharge toThis the grid. Three battery sizes under the penetration rateofcapacity ofV2G 90% and the rate from rates offor electric vehicle. This article developed an agent-based model tobattery estimate the real real amount of electricity that research topic. Previous studies mainly focused onagent-based the maximum potential capacity, which, however, is vehicles can discharge to the Three battery sizes under of 90% and penetration rate from potential capacity kW toresults 1868.5 kW and thatthe increase 10% to 90% inthe penetration rate 10% to unrealistic 90% werefrom The show that increase offrom battery capacity led to an ofcould V2G Abstract usually toinvestigated. achieve. The potential capacity of the V2G ispenetration relevant to rate the size of batteries andincrease the penetration vehicles can discharge to1650.2 the grid. grid. Three battery sizes under the penetration rate of 90% and the penetration rate from 10% to 90% were investigated. The results show that the of battery capacity led an of V2G Keywords: agent-based model; electric vehicle; vehicle-to-grid potential; battery size; penetration rate; account for V2G potential capacity change from 162.8 toincrease 1650.2 kW. potential capacity from 1650.2 kW to 1868.5 kW andkW that increase from 10% to 90% in to penetration rate 10% to electric 90% were investigated. The results show that the increase of battery capacity led to anofincrease increase of could V2G rates of vehicle. This article developed an agent-based model to estimate the real amount electricity that potential capacity from 1650.2 kW to 1868.5 kW and that increase from 10% to 90% in penetration rate could account for V2G potential change from 162.8 kW to increase 1650.2 Districtvehicles heatingcan networks aretocommonly addressed insizes theand literature as onekW. of the most solutions for decreasing the discharge thecapacity grid.kW Three battery under the penetration rate of 90% and in thepenetration penetration rate from potential capacity from 1650.2 to 1868.5 kW that from 10% to effective 90% rate could account for capacity change from 162.8 kW to 1650.2 kW. Copyright ©V2G 2018 Elsevier Ltd. All rights reserved. Keywords: agent-based model; electric vehicle; vehicle-to-grid potential; battery size; penetration rate; greenhouse gas emissions from the building sector. These systems high investments 10% to 90% werepotential investigated. The results show that the increase of battery capacitywhich led toare anreturned increasethrough of V2Gthe heat account for V2G potential capacity change from 162.8 kW torequire 1650.2 kW. Copyright ©changed 2018 Elsevier Ltd. All rightsand reserved. and peer-review under responsibility of the committee of Applied Energy Symposium anddecrease, Keywords: agent-based model; electric vehicle; vehicle-to-grid potential; battery size;10% penetration rate; sales. Selection Due to the climate conditions building renovation policies, heat the futurerate could potential capacity from 1650.2 kW to 1868.5 kW andscientific that increase from todemand 90% in in penetration could Selection and peer-review under responsibility of the scientific committee the CUE2018-Applied Energy Keywords: agent-based model; electric vehicle; vehicle-to-grid potential; battery size;of penetration rate; Forum 2018: Low carbon cities and urban energy systems, CUE2018. Copyright © 2018 Elsevier Ltd. All rights reserved. prolonging the for investment return period. Keywords: agent-based model; electric change vehicle; vehicle-to-grid potential; battery account V2G potential capacity from 162.8 kW toenergy 1650.2 kW.size; penetration rate; Symposium and Forum 2018: Low carbon cities andthe urban systems. Selection peer-review under responsibility scientific committee of Applied Energyfunction Symposium anddemand Copyright © 2018 Elsevier Ltd. All rights reserved. The main scope ofand this paper is to assess the feasibility ofofusing the heat demand – outdoor temperature for heat Copyright © 2018 Elsevier Ltd. All rights reserved. KeyClick on Microsoft Word Objects Forum 2018: Low carbon cities and urban energy systems, CUE2018. Selection and peer-review under responsibility of the scientific committee of Applied Energy Symposium and of 665 Copyright © 2018 Elsevier Ltd. All rights reserved. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted Keywords: agent-based model; electric vehicle; vehicle-to-grid potential; battery size; penetration rate; Selection and peer-review under responsibility of the committee of Energy and Forum Low carbon cities andperiod urbanand energy systems, CUE2018. Selection peer-review under responsibility of the scientific scientific committee of Applied Applied Energy Symposium Symposium and district buildings that 2018: varyand in both construction typology. Three weather scenarios (low, medium, high) and three Forum 2018: Low carbon cities and urban energy systems, CUE2018. KeyClick on Microsoft Word Objects Forum 2018: cities and energy systems, CUE2018. Copyright © Low 2018 Elsevier Ltd.(shallow, All urban rightsintermediate, reserved. renovation scenarios werecarbon developed deep). To estimate the error, obtained heat demand values were KeyClick onand Microsoft Word Objects Selection peer-review under of the scientific committee AppliedbyEnergy Symposium and compared with results from a dynamic heatresponsibility demand model, previously developed andofvalidated the authors. KeyClick on Microsoft Word Objects KeyClick on Microsoft Word Objects Forum 2018: Low carbon cities and urban energy systems, CUE2018. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications 1. Introduction (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation KeyClick on Microsoft Word Objects scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.Renewable Introduction energy access to on theaverage energywithin hub istheessential premise the per energy hubthat economical The value of slope coefficient increased range of to 3.8% up to 8% decade, corresponds to the 1. in Introduction operation, so providing enough energy storage facilities is crucial to smooth the power grid. Usually, decrease the number of heating hours of 22-139h during the heating season (depending on the combination of weather and 1. Introduction energy the hand, energy hub isintercept essential premise the energy hub economical 1. Renewable Introduction matched special storage access facilities needed to be equipped withincreased antoenergy it’sper expensive and has on the renovation scenarios considered). On thetoother function forsystem, 7.8-12.7% decade (depending operation, soThe providing enough storage is vehicle(EV) crucial toparameters smooth power grid. Usually, Renewable energy access toenergy the energy hub iselectric essential to premise energy hub economical coupled scenarios). values suggested could be used tofacilities modify the function for the scenarios considered, and high maintenance costs. More attentions are drawn to andthe thethe vehicle-to-grid (V2G) Renewable energy access to the energy is to premise the energy hub economical 1. Introduction matched special storage facilities needed to behub equipped an energy system, it’s expensive and has operation, so of providing enough storage facilities isEV crucial to smooth power Usually, Renewable energy access to energy the energy hub is essential essential to premise the the energy hubgrid. economical improve the accuracy heat demand estimations. technology. The technologies have developed quickly andwith ownership has soared with encourage

operation, so enough energy storage facilities is crucial to the power grid. Usually, high maintenance costs. More attentions to electric vehicle(EV) andtothe vehicle-to-grid (V2G) matched storage facilities neededare todrawn beare equipped with an energy system, and has operation, so providing providing enough energy storage facilities is to crucial to smooth smooth theit’s power Usually, policies in special recent years[1]. In this contract, EVs projected contribute up 90% ofexpensive totalgrid. private car matched special storage facilities needed to be equipped with an energy system, it’s expensive and has Renewable energy to the energy hub isto essential toownership premise the energy hub economical © 2017 The Authors. Published bydemanding Elsevier Ltd. technology. The technologies have developed quickly and EV hasthe soared with encourage high maintenance costs. More attentions are drawn electric vehicle(EV) and vehicle-to-grid (V2G) matched special storage facilities needed tosmall-scale be equipped with an energy system, it’s expensive and has ownership, creating extraaccess on the electric network, including during peak demand high maintenance costs. More attentions are drawn to electric vehicle(EV) and the vehicle-to-grid (V2G) Peer-review under responsibility the Scientific Committee of to The 15th International Symposium onof District Heating and operation, so providing enough energy storage facilities isdemand crucial to smooth the power grid. Usually, policies in recent years[1]. In this contract, are projected to contribute up tothe 90% total private car technology. The technologies have developed quickly and EV ownership soared with encourage high maintenance costs.of More attentions areEVs drawn electric vehicle(EV) and vehicle-to-grid (V2G) hours. One opportunity to manage increasing costs and spikes is has the utilization act as an technology. The technologies have developed quickly and EV ownership has soared with encourage Cooling. matched special storage facilities needed to be equipped with an energy system, it’s expensive and has ownership, creating extraproviding demanding onshifting theEVs small-scale electric including during peak demand policies inenergy recent years[1]. In this contract, arethe projected to network, contribute uphas to 90% of when total private car technology. The technologies have developed quickly and EV ownership soared with encourage aggregated store, peak to energy hub and supply electricity vehicles policies in years[1]. In this contract, EVs are to contribute up to of total private high maintenance costs. attentions drawn toprojected electric vehicle(EV) and the vehicle-to-grid (V2G) hours. One opportunity todemanding increasing costs and demand spikes is the the90% utilization act as car an ownership, creating extraMore onofare the small-scale electric including during peak demand policies in recent recent years[1]. Inmanage this contract, EVs are projected to network, contribute up to 90% of total private car are accessing to the grid[2]. The amount EVs is relatively larger in a distract, total storage capacity Keywords: Heat demand; Forecast; Climate change ownership, creating extra demanding on the small-scale electric network, including during peak demand technology. The have developed quickly and EVhub ownership has soared with encourage aggregated store, peak to the and energy and supply electricity when vehicles hours. Oneenergy opportunity to manage increasing costs demand spikes is the utilization act asonan ownership, creating extraproviding demanding on the electric network, including during peak demand corresponding the technologies aggregated EV cells is shifting realsmall-scale compelling. The project which the technology based hours. One opportunity to manage increasing costs demand spikes utilization act as an policies in recent years[1]. InThe thisamount contract, EVs are projected tohub contribute upis 90% ofZhoushan total private are accessing to in the grid[2]. ofshifting is relatively larger inand a in distract, total storage capacity aggregated energy store, providing peak to theand energy supply electricity when vehicles hours. One opportunity to manage increasing costs and demand spikes istothe the utilization act as car an V2G participate energy storage has been put into use in microgrids Denmark and City, aggregated energy store, providing peak shifting to the energy hub and supply electricity when vehicles ownership, extra providing demanding on isthe small-scale electric network, including during peakbased demand corresponding EV cells real compelling. The project which the on are accessing tothe theaggregated grid[2]. The amount of EVs istorelatively larger inand a distract, the technology total storage capacity aggregated energy store, peak shifting the energy hub supply electricity when vehicles China[2, 3]. creating are accessing to the grid[2]. The amount of EVs is relatively larger in aa distract, the utilization total storage capacity hours. Onetoopportunity manage increasing costs and the act as an V2G participate in aggregated energy has been putcompelling. into in demand microgrids inwhich Denmark and real-time Zhoushan City, corresponding EV cells is real The project the technology based on are accessing tothe the grid[2]. The amount of system EVs is relatively larger inspikes distract, total storage capacity The key realize V2Gtostorage used in energy is use accurate assessment ofisEV cell’s V2G corresponding the aggregated EV cells is real compelling. The project which the technology based on 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. aggregated energy store, providing peak shifting to the energy hub and supply electricity when vehicles China[2, 3]. theinaggregated V2G participate energy storage has been putcompelling. into use in The microgrids Denmark and Zhoushan City, corresponding EV cells is real projectinwhich the technology based on Peer-review under responsibility of the Scientific Committee of put The into 15th International Symposium on District Heating and Cooling.City, V2G participate in energy storage has been use in microgrids in Denmark and Zhoushan are accessing torealize the grid[2]. The amount of EVs relatively in a distract, the cell’s total capacity 1876-6102 Copyright ©to 2018 Elsevier Ltd.storage All rights reserved. The key V2G used inhas energy system isuse accurate assessment of EV real-time V2G China[2, 3]. V2G participate in energy been putisinto in larger microgrids in Denmark andstorage Zhoushan City, 1876-6102 Copyright © 2018responsibility Elsevier Ltd. All rights reserved.committee of the CUE2018-Applied Energy Symposium and Forum Selection and peer-review under the is scientific China[2, 3]. corresponding the aggregated EVinof cells real compelling. The assessment project which the technology basedV2G on Theand key toand realize V2G used energy system is accurate EV cell’s China[2, 3]. Selection peer-review under responsibility of the scientific committee of the Applied Energyof Symposium andreal-time Forum 2018: 2018: Low carbon cities urban energy systems. The key to realize V2G used in energy system is accurate assessment of EV cell’s real-time V2G V2G participate in energy storage has been put into use in microgrids in Denmark and Zhoushan City, The key toandrealize V2G used energy system is accurate assessment of EV cell’s real-time V2G 10.1016/j.egypro.2018.09.222 Low carbon cities urban energy systems, CUE2018. 1876-6102 Copyright © 2018 Elsevier Ltd.inAll rights reserved. China[2, 3].peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Selection and 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. The key to and realize V2G used inAll energy system committee is accurate of Symposium EV cell’s and real-time V2G 1876-6102 Copyright © 2018 Elsevier Ltd. rights reserved. Low carbon cities urban energy systems, CUE2018. Selection and peer-review under responsibility of the scientific of theassessment Applied Energy Forum 2018: 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection andcities peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon and urban energy systems, CUE2018. Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018.



Author name / Energy Procedia 00 (2018) 000–000 Author name / Energy Procedia 00 (2018) 000–000 Author name / Energy Procedia 00 (2018) 000–000 Author name / Energy Procedia 00 (2018) 000–000 capacity. Two factors are Author analyzed in[4]: (1)the Procedia current-carrying the wires and other name / Energy 00 (2018)capacities 000–000 of capacity. Two factors are Author analyzed in[4]: (1)the Procedia current-carrying capacities of the wires and other name / Energy 00 (2018) 000–000 circuitry connected vehicle throughYiling the building to the grid; (2)the stored energy in the wires vehicle, divided capacity. Two factors are analyzed in[4]: (1)the current-carrying capacities of and other 631 Liu/ Energy et(1)the al. / to Energy Procedia 152 (2018) 630–635 circuitry connected vehicle throughname the building the grid; (2)the stored energyofinthe the wires vehicle, divided Author Procedia 00 (2018) 000–000 capacity. Two are the analyzed in[4]: current-carrying capacities the other by the time it isfactors used. And authorthe developed equations to (2)the calculate the energy limit oninV2G by lineand capacity circuitry connected vehicle through building to the grid; stored the vehicle, divided capacity. Two are the analyzed in[4]: (1)the current-carrying capacities ofinV2G the wires other by the time it isfactors used. And authorthe developed equations to (2)the calculate the energy limit on by lineand capacity circuitry connected vehicle through building to the grid; stored the vehicle, divided andthe bytime theTwo vehicle’s stored energy. this paper only uses 20capacities miles for on distance forline the battery, capacity. are the analyzed in[4]: (1)the current-carrying of the wires and other by it isfactors used. 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Andminimalist, the authorHowever, developed equations to (2)the calculate thepower limit on V2G by line capacity the driving are and it only calculates achievable capacity, not real-time and by the vehicle’s stored energy. this paper only uses 20 miles for distance for the battery, circuitry connected vehicle through the building to the grid; stored energy in the vehicle, divided estimation and [5-7] adoptdeveloped adaptive Kalman filter, radial-based function neural network and by the itpatterns isscheduling. used.stored Andminimalist, the author equations to calculate thepower limit V2G by line capacity the driving are and it only calculates achievable capacity, not and bytime the and vehicle’s energy. However, this paper only uses 20 miles for on distance for thereal-time battery, estimation [5-7] adopt adaptive Kalman filter, radial-based function neural network and the driving are and itmethods only calculates achievable capacity, not real-time by the time itpatterns isscheduling. used. Andminimalist, the author developed equations to calculate the limit on V2G by line capacity enhanced coulomb counting three different estimate the state of power charge(SOC) of EV cells, and by the vehicle’s stored energy. However, this paper only uses 20 miles for distance for the battery, estimation and scheduling. [5-7] adopt adaptive Kalman filter, radial-based function neural network and the driving patterns are minimalist, and it only calculates achievable power capacity, not real-time enhanced coulomb counting three different methods estimate the state of charge(SOC) of EV cells, and estimation and scheduling. [5-7] adopt adaptive Kalman filter, radial-based function neural network and and driving by thecoulomb vehicle’s stored energy. However, this paper onlythe uses 20It’s miles for that distance for the battery, then calculate the spare capacity which could provide with grid. acharge(SOC) pity these researches all the patterns are minimalist, and it only calculates achievable power capacity, not real-time enhanced counting three different methods estimate the state of of EV cells, and estimation and scheduling. [5-7] adopt adaptive Kalman filter, radial-based function neural network and then calculate the spare capacity which could provide with the grid. It’s a pity that these researches all enhanced coulomb counting three different methods estimate the state of power charge(SOC) EV cells, and the driving patterns are minimalist, and itthe only calculates achievable capacity, not real-time focus on single type of capacity cell. [8] adopt suggests impact of large-scale plug-in vehicles onofthe renewable estimation and scheduling. [5-7] adaptive Kalman filter, radial-based function neural network and then calculate the spare which could provide with the grid. It’s a pity that these researches all enhanced coulomb counting three different methods estimate the state of charge(SOC) of EV cells, and focus on single type of cell. [8] suggests the impact of large-scale plug-in vehicles on the renewable then calculate the spare capacity which could provide with grid. It’s a pity that these researches all estimation and scheduling. [5-7] adopt adaptive Kalman filter, radial-based function neural network and micro-grids, introducing a stochastic framework to model thethecharging request. enhanced coulomb counting three different methods estimate the state of charge(SOC) of EV cells, and focus on single type of cell. [8] suggests the impact of large-scale plug-in vehicles on the renewable then calculate thetype spare capacity which could provide with grid. It’s acharge(SOC) pity that these researches all micro-grids, introducing a stochastic framework to model thethecharging request. focus on single of cell. [8] suggests the impact of large-scale plug-in vehicles on the renewable enhanced coulomb counting three different methods estimate the state of of EV cells, and The car driving patterns are highly random[9], to simulate the behavior accurately and the then calculate thetype spare capacity which could provide with grid.driving It’s a pity that these researches all micro-grids, introducing a stochastic framework to model thethecharging request. focus on single of cell. [8] suggests the impact of large-scale plug-in vehicles on the renewable The car driving patterns are highly random[9], to simulate the driving behavior accurately and the micro-grids, introducing atostochastic model thethecharging request. then calculate the spare capacity which could provide with grid. It’s a pity that these researches all driving patterns is crucial realize theframework capacity oftoV2G’s real-time estimation. [10]proposed arenewable dynamicfocus on single type of cell. [8] suggests the impact of large-scale plug-in vehicles on the The car driving patterns are highly random[9], to simulate the driving behavior accurately and the micro-grids, introducing atostochastic model the charging request. driving patterns is crucial realize theframework capacity oftoV2G’s real-time estimation. [10]proposed arenewable dynamicTheoncar driving patterns are highly random[9], to manage simulate thecharging driving behavior accurately and the focus single type ofswarm cell. [8] suggests the impact of large-scale plug-in vehicles on the neighborhood particle optimization method to the profiles based onareal-world micro-grids, introducing atostochastic model the charging request. driving patterns is crucial realize theframework capacity oftoV2G’s real-time estimation. [10]proposed dynamicThe car driving patterns are highly random[9], simulate the driving behavior accurately and the neighborhood particle swarm optimization method to manage the charging profiles based on real-world driving patterns is crucial to realize the capacity of V2G’s real-time estimation. [10]proposed a dynamicmicro-grids, introducing a stochastic framework to model the charging request. EVThe charging data, itpatterns uses the Monte Carlo simulation method. The vehiclebehavior drivingbased analyze inand [11]the is car driving are highly random[9], simulate the driving accurately neighborhood particle swarm optimization method to manage the charging profiles onareal-world driving patterns is crucial to realize the capacity of V2G’s real-time estimation. [10]proposed dynamicEV charging data, it uses the Monte Carlo simulation method. The vehicle driving analyze in [11]the is neighborhood particle swarm optimization method to mostly manage the charging profiles based real-world The driving patterns are highly random[9], simulate the driving behavior accurately and based oncar vehicle trip chain. These existing researches adopt mathematical method toonsimulate the driving patterns is crucial to realize the capacity of V2G’s real-time estimation. [10]proposed a dynamicEV charging data, it uses the Monte Carlo simulation method. The vehicle driving analyze in [11] is neighborhood particle swarm optimization method to manage the charging profiles based on real-world based on vehicle trip chain. These existing researches mostly adopt mathematical method to simulate the EV charging data, ithowever, uses Monte simulation method. The vehicleprofiles driving analyze in [11] is driving patterns istrip crucial tothe realize theCarlo capacity of V2G’s real-time estimation. [10]proposed areal-world dynamiccharging behaviors, conventional method usually cannot sufficiently consider the randomness neighborhood particle swarm optimization method to manage the charging based on based on vehicle chain. These existing researches mostly adopt mathematical method to simulate the EV charging data, uses the Monte Carloresearches simulation method. The vehicleprofiles driving analyze in [11]the is charging behaviors, conventional method usually cannot sufficiently consider the randomness based on vehicle tripithowever, chain. These existing mostly adopt mathematical method toonsimulate neighborhood particle swarm optimization method to manage thesettings. charging based real-world of charging behaviors andthe lack flexibility of adjusting model Agent-based model has[11] been EV charging data, ithowever, uses Monte Carloresearches simulation method. The vehicle driving analyze in is charging behaviors, conventional method usually cannot sufficiently consider the randomness based on vehicle trip chain. These existing mostly adopt mathematical method to simulate the of charging behaviors and lack flexibility of adjusting model settings. Agent-based model has been charging behaviors, conventional method usually cannot sufficiently consider the randomness EV charging data, ithowever, uses the Monte Carloresearches simulation method. The vehicle driving analyze in [11] is proved tovehicle effectively take full consideration ofadjusting randomness of human activities and easily account for based on trip chain. These existing mostly adopt mathematical method to simulate the of charging behaviors and lack flexibility of model settings. Agent-based model has been charging behaviors, conventional method usually cannot sufficiently consider the randomness proved effectively take consideration randomness of human activities and easily account for of charging behaviors andfull lack flexibility ofofadjusting model settings. Agent-based model has been based onto vehicle triphowever, chain. These existing researches mostly adopt mathematical method to simulate the various system settings[12]. charging behaviors, however, conventional method usually cannot sufficiently consider the randomness proved to effectively take full consideration ofadjusting randomness of human activities and easily account for of charging behaviors and lack flexibility of model settings. Agent-based model has been various system settings[12]. proved tobehaviors, effectively take fulltoconventional consideration of randomness of human activities and account for charging however, method usually cannot sufficiently consider the randomness Therefore, the paper aims study the influence of battery size and penetration rateeasily on V2G potential of charging behaviors and lack flexibility of adjusting model settings. Agent-based model has been various system settings[12]. proved to effectively take full ofadjusting randomness of human activities and account for Therefore, the paper aims toconsideration study the influence of battery size and penetration rateeasily on V2G potential various system settings[12]. of charging behaviors and lack flexibility of model settings. Agent-based model has been capacity. To this aim, anaims agent-based was established simulate an energyand system, including an proved to effectively take fulltoconsideration of randomness of human activities easily account for Therefore, the paper study model the influence of batteryto size and penetration rate on V2G potential various system settings[12]. capacity. To this aim, an agent-based model was established to simulate an energy system, including an Therefore, the paper aims toconsideration study the ofarticle batterywith size and penetration rate on V2G potential proved to effectively take full of randomness of human activities easily account for energy hub and numbers of EV agents. Weinfluence begin this the description ofand the structure of V2Gvarious system settings[12]. capacity. To this aim, an agent-based model was established to simulate an energy system, including an Therefore, the paper aims to study the influence of battery size and penetration rate on V2G potential energy hub and numbers of EV agents. We begin this article with the description of the structure of V2Gcapacity. To thissettings[12]. aim, agent-based model was In established todescribes simulate an system,model including an various system equipped energy hub an and the concept ofinfluence V2G. section 3, theenergy agent-based for EV Therefore, the paper aims to study the of battery size and penetration rate on V2G potential energy hub and numbers of EV agents. We begin this article with the description of the structure of V2Gcapacity. To this aim, agent-based model was In established to simulate an system, including an equipped energy hub an and the concept ofinfluence V2G. section 3, describes theenergy agent-based model for EV energy hub and numbers of EV agents. We begin this article with the description of the structure of V2GTherefore, the paper aims to study the of battery size and penetration rate on V2G potential agents and the overall energy system. this we simulate the an V2G capacity inmodel different capacity. To thisnumbers aim, agent-based model waspaper, established todescribes simulate energy system, including an equipped energy hub an and the concept ofIn V2G. In section 3, the agent-based for EV energy hub and of EV agents. We begin this article with the description of the structure of V2Gagents and the overall energy system. In this paper, we simulate the V2G capacity in different EV equipped energy hubsize andagent-based concept of finally V2G. In section 3,todescribes theenergy agent-based model for EV capacity. To thisthree aim, an model waspaper, established simulate an system, including an penetration for ofthe vehicle cells, we draw conclusion about the factors. energy hub and numbers of EV agents. We begin this article with the description of the structure of V2Gagents and the overall energy system. In this we simulate the V2G capacity in different EV equipped hubsize and concept ofInfinally V2G. In 3, describes the agent-based EV penetration for ofthe vehicle cells, wesection draw about the factors. agents hub andenergy the three overall energy system. this paper, weconclusion simulate the V2G capacity inmodel different EV energy and numbers of EV agents. We begin this article with the description of the structure offor V2Gequipped energy hubsize and concept ofInfinally V2G. In 3, describes the agent-based model for EV penetration for three ofthe vehicle cells, wesection draw conclusion about the factors. agents and the overall energy system. this paper, we simulate the V2G capacity in different EV penetration for three size of vehicle cells, finally we draw conclusion about the factors. equipped energy hub and the concept of V2G. In section 3, describes the agent-based model for EV 2. Theand structure of V2G-equipped energy hub agents the three overall energy system. Infinally this paper, weconclusion simulate the V2G capacity in different EV penetration for size of vehicle cells, we draw about the factors. 2. The structure of V2G-equipped energy hub agents the three overall system. this paper, weconclusion simulate the V2G penetration for sizeenergy of vehicle cells,Infinally we draw about thecapacity factors. in different EV 2. Theand structure of V2G-equipped energy hub 2. The Thebasic structure of V2G-equipped energy hub penetration forconcept three size of vehicle cells, finally we draw conclusion about the factors. of V2G is that EVs provide power to the grid while parked. Battery can charge 2. The Thebasic structure of V2G-equipped energy concept of V2G is that EVs hub provide power to the grid while parked. Battery can charge during the valley time and discharge when power ispower needed. vehicle this energy needcan to charge satisfy 2. The structure of V2G-equipped energy The basic concept of V2G is that EVs hub provide to Each the grid whilein Battery during the valley time and discharge when power ispower needed. vehicle inparked. this energy needcan to charge satisfy The basic of V2G is that EVs provide to Each the flow; grid while parked. 2. The structure of V2G-equipped energy hub three points asconcept below: (1) adischarge connection to the grid isforneeded. electricity (2) control or Battery logical connection during the valley time and when power Each vehicle in this energy need to satisfy The basic concept of V2G is that EVs provide power to the grid while parked. Battery can charge three points as below: (1) a connection to the grid for electricity flow; (2) control or logical connection during the for valley time and discharge when power is needed. Each vehicle in this energy need to satisfy necessary communication with the grid operator; (3)tocontrol andwhile metering on-board vehicles[4]. The basic concept of V2G is that EVs provide power the grid parked. Battery can charge three points as below: (1) a connection to the grid for electricity flow; (2) control or logical connection during the valley time of and when power needed. Each vehicle inparked. this energy need to charge satisfy necessary for communication with the operator; (3)tocontrol andwhile metering on-board vehicles[4]. three asconcept below: (1) adischarge connection togrid the grid is for electricity flow; (2) control or Battery logical connection Thepoints basic V2G is that EVs provide power the grid can Electricity flows back the grid from EV battery, the flow isEach two-way (shows in Fig.1as lines with two during the valley time to and discharge when power is needed. vehicle in this energy need to satisfy necessary for communication with the grid operator; (3) control and(2) metering on-board vehicles[4]. three points as below: (1) a connection to the grid for electricity flow; control or logical connection Electricity flows back to the grid from EV battery, the flow is two-way (shows in Fig.1as lines with two necessary communication withsignal the grid operator; (3) control and metering on-board vehicles[4]. during theInfor valley time to and discharge when power needed. Each vehicle in this energy need to satisfy arrows). this paper, thethe control from theis grid operator (labeled ISO, forFig.1as Independent System three points as below: (1) a connection to the grid for electricity flow; (2) control or logical connection Electricity flows back grid from EV battery, the flow is two-way (shows in lines with two necessary for communication with the grid operator; (3) control and metering on-board vehicles[4]. arrows). In this paper, the control signal from the grid operator (labeled ISO, for Independent System Electricity flows back to the grid from EV battery, the flow private is two-way (shows in Fig.1as lines with two three points ascommunication below: (1) acontrol connection togrid the grid for electricity flow; (2) control or logical connection Operator) through a third-party aggregator of dispersed vehicles’ power, to each individual necessary for with the operator; (3) control and metering on-board vehicles[4]. arrows). In this paper, the signal from the grid operator (labeled ISO, for Independent System Electricity flows back tothethecontrol grid from battery, the is two-way in lines with two Operator) through a third-party aggregator of dispersed private vehicles’ power, to each individual arrows). thiscommunication paper, signal from the gridflow operator (labeled ISO, forFig.1as Independent System necessary with theEV grid operator; (3) control and (shows metering on-board vehicles[4]. vehicle. Infor Electricity flows back tothethecontrol grid from EV battery, the flow is two-way (shows in Fig.1as lines with two Operator) through a third-party aggregator of dispersed private vehicles’ power, to each individual arrows). In this paper, signal from the grid operator (labeled ISO, for Independent System vehicle. Operator) through a propose third-party aggregator of dispersed private vehicles’ to each individual Electricity flows back tothethecontrol grid EVfrom battery, the flow is two-way (shows in Fig.1as lines with two In thisInpaper, we the from conception ofthe V2G’s available capacity topower, represent the capacity of arrows). this paper, signal grid operator (labeled ISO, for Independent System vehicle. Operator) through third-party of dispersed private(labeled vehicles’ to each individual In thisInpaper, wea propose the aggregator conception ofthe V2G’s available capacity topower, represent the capacity of vehicle. arrows). this paper, the control signal from grid operator ISO, for Independent System aggerated EV battery participating energy hub’s management, as storage elements. The aim is to realize Operator) through a third-party aggregator of dispersed private vehicles’ power, to each individual In this paper, we propose the conception V2G’s available capacity to represent the capacity of vehicle. aggerated EV battery participating energy hub’s management, as storage elements. The aim is to realize In this paper, wecapacity. thewe conception V2G’s available capacity topower, represent theofcapacity of Operator) through a propose third-party aggregator dispersed private vehicles’ to each individual maximum of V2G So, develophub’s anofagent-based model to simulate energy flow the energy vehicle. aggerated EV battery participating energy management, as storage elements. The aim is to realize In this paper, wecapacity. propose theweconception V2G’s available capacity to energy represent theofis capacity of maximum of V2G So, develophub’s anofagent-based model to simulate flow the energy aggerated EV battery participating energy management, as storage elements. The aim to realize vehicle. hubInand the capacity of The fundamental components this model are various agents and thisanalyze paper, wecapacity. propose theV2G. V2G’s available capacity to energy represent theofis capacity of maximum of V2G So, weconception develop anofagent-based model toof simulate flow the aggerated EV battery participating energy hub’s management, as storage The aim to energy realize hub the capacity of The fundamental components thiselements. model are various agents and maximum of V2G So, weconception develop anofagent-based model toofsimulate flow the energy Inand thisanalyze paper, wecapacity. propose theV2G. V2G’s available capacity to energy represent theofis capacity of their behaviors. aggerated EV battery participating energy hub’s management, as storage elements. The aim to realize hub and analyze the capacity of V2G. The fundamental components of this model are various agents and maximum of V2G capacity. So, weenergy develop an agent-based model toofsimulate energy flow ofisagents the energy their behaviors. hub and analyze the capacity of V2G. The fundamental components this model are various and aggerated EV battery participating hub’s management, as storage elements. The aim to realize maximum of V2G we develop an agent-based model toofsimulate energy flow of agents the energy theirand behaviors. hub analyze thecapacity. capacitySo, of V2G. The fundamental components this model are various and their behaviors. maximum of V2G capacity. So, we develop an agent-based model toofsimulate energy flow of agents the energy 3. and Model description hub analyze the capacity of V2G. The fundamental components this model are various and their behaviors. 3. Model description hub analyze the capacity of V2G. The fundamental components of this model are various agents and their behaviors. 3. and Model description 3. This Model description their behaviors. paper develops a multi-agent model to simulate operation of the energy hub and investigate the 3. This Model description paper develops a multi-agent model to simulate operation of the energy hub and investigate the influence ofdescription battery sizeaand penetration on the V2G capacity. The of model is builthub in Anylogic7.3.6, 3. Model This paper develops multi-agent model to simulate operation the energy and investigatewith the influence of battery sizeaand penetration on the V2G capacity. The of model is builthub in Anylogic7.3.6, This paper multi-agent to simulate operation the energy and investigatewith the 3. Model description a time step of 1develops minute. The details ofmodel EVonagents are capacity. described as below. influence of battery size and penetration the V2G The model is built in Anylogic7.3.6, with Thisstep paper multi-agent to simulate operation of the energy and investigatewith the ainfluence time 1develops minute. details ofmodel EVonagents are capacity. described as below. ofofbattery sizeaThe and penetration the V2G The model is builthub in Anylogic7.3.6, Thisstep paper aThe multi-agent to simulate operation of the energy hub and investigatewith the a time ofbattery 1develops minute. details ofmodel EVonagents are capacity. described as below. influence of size and penetration the V2G The model is built in Anylogic7.3.6, a3.1 time step of 1develops minute. details ofmodel EV agents are described as below. This paper multi-agent to simulate operation of the energy hub and investigatewith the The behaviors ofsize EVsaThe influence of battery and penetration on the V2G capacity. The model is built in Anylogic7.3.6, a3.1 time step ofbattery 1 minute. details of EVonagents are capacity. describedThe as below. The behaviors ofsize EVsThe influence of and penetration the V2G model is built in Anylogic7.3.6, with a3.1 time step of 1 minute. The behaviors of EVsThe details of EV agents are described as below. The behaviors of EVsThe details of EV agents are described as below. a3.1 time step of simulate 1 minute. EV agents the driving behaviors and calculate the corresponding SOC of battery in any time, 3.1EV The behaviors of EVs agents simulate the driving behaviors and calculate the corresponding SOC of battery in any time, 3.1 The behaviors of EVs EV agents simulate the drivingcharging behaviors andEach calculate the corresponding SOC of battery in any time, and then simulate their real-time load. EV agent has six different states: at home, driving, 3.1 The behaviors of EVs agents simulate the drivingcharging behaviors andEach calculate the corresponding SOC of battery in any time, andEV then simulate their real-time load. EV agent has six different states: at home, driving, EV agents simulate driving behaviors andEach calculate thewhile corresponding SOC of battery in any time, and then simulate theirthe real-time charging load. EV agent has six different states: at home, driving, outside, normal charging, rapid charging and discharging connected to the grid. The driving andEV then simulate theirthe real-time load. EV agent has six different at home, driving, agents simulate driving behaviors andEach calculate thewhile corresponding SOC of battery in any time, outside, normal charging, rapid charging charging and discharging connected tostates: the grid. The driving and then simulate theirthe real-time charging load. EV agent has six different at home, driving, outside, normal charging, rapid charging and discharging connected tostates: the grid. The driving EV agents simulate driving behaviors andEach calculate thewhile corresponding SOC of charging battery in or any time, behavior is simulated by a probabilistic statistical model, and the decision of not is outside, charging, chargingload. and discharging while connected thecharging grid. Theordriving, driving and then normal simulate their real-time charging Each EV agent has six different at home, behavior is simulated by rapid a probabilistic statistical model, and the decisiontostates: of not is outside, normal charging, rapid charging and discharging while connected to the grid. The driving behavior is simulated by a probabilistic statistical model, and the decision of charging or not is and then simulate their real-time charging load. Each EV agent has six different states: at home, driving, determined by the probabilistic algorithm. The description of driving patterns is in [13]. behavior is by simulated by rapid a probabilistic statistical model, and the decision charging not is outside, normal charging The and discharging connected the grid. Theor driving determined thecharging, probabilistic algorithm. description ofwhile driving patterns istoinof [13]. behavior is by simulated a probabilistic statistical model, and the decision of charging or driving not is determined thecharging, probabilistic The description ofwhile driving patterns istoin [13]. EV agent model’ data by source isalgorithm. the National Household Travel Survey(NHTS), 2009[14]. this model, outside, normal rapid charging and discharging connected the grid. In The EV agent model’ data by source isalgorithm. the National Household Travel Survey(NHTS), 2009[14]. In this determined the probabilistic The description of driving is in behavior is by simulated a probabilistic statistical model, and thepatterns decision of[13]. charging or model, not is EV agent model’ data source is the National Household Travel Survey(NHTS), 2009[14]. In this model, determined the probabilistic algorithm. The description of driving is in behavior is by simulated by a probabilistic statistical model, and thepatterns decision of[13]. charging or model, not is EV agent model’ data source is the National Household Travel Survey(NHTS), 2009[14]. In this determined by the probabilistic algorithm. The description of driving patterns is in [13].

Author name / Energy Procedia 00 (2018) 000–000 Author name / Energy Procedia 00 (2018) 000–000

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name / Energy 00 popular (2018) 000–000 the EV agent is assumed toAuthor be three types of EV,Procedia which are vehicle brands in American car name / Energy 00 popular (2018) 000–000 the EV agent is assumed toAuthor be three types of EV,Procedia which are vehicle brands in American car name / Energy Procedia 00 (2018) 000–000 market, have three differentAuthor batteries. Basic parameters are given in Table 1. Yiling Liu et al. / Energy Procedia 152 (2018) 630–635 market, have three different batteries. Basic parameters are given in Table 1. brands in American car Author name / Energy Procedia 00 (2018) 000–000 the EV agent is assumed to be three types of EV, which are popular vehicle the EV agent is assumed to be three types of EV, which are popular vehicle in American car market, have three differenttobatteries. parameters are are given in Table 1. brands Table Basic of EVA the EV agent is assumed be three Basic types1. of EV,parameters which popular vehicle brands in American car Table 1.parameters Basic parameters of EVA market, have three different batteries. Basic are given in Table 1. the EV agent is assumed be three Basic types parameters of EV, which popular vehicle market, have three differenttobatteries. are are given in Table 1. brands in American car Table 1.parameters Basic parameters of EVA in Table 1.Rapidly Type have three Battery EnergyBasic Charging Normal V2G market, different batteries. are given Type Battery Energy Charging Normal Rapidly V2G 1. Basic parameters of EVA capacity cost Table efficiency charging charging Efficiency Table 1. Basic parameters of EVA capacity cost efficiency charging charging Efficiency Type Battery Energy Charging Normal Rapidly V2G Table 1. Basic EVA (kWh) (kWh/km) (%) parameters of power(kW) power(kW) (%) (kWh) (kWh/km) (%) power(kW) power(kW) (%) Type Battery Energy Charging Normal Rapidly V2G capacity cost efficiency charging charging Efficiency Chevrolet Bolt Battery 60 0.187 Type Energy Charging Normal Rapidly V2G capacity cost efficiency charging charging Efficiency Chevrolet Bolt 60 0.187 Type Battery Energy Charging Normal Rapidly V2G (kWh) (kWh/km) (%) power(kW) power(kW) (%) Tesla Model S capacity 85 0.177 86.4 3.7 44 96.16 cost efficiency charging charging Efficiency (kWh) (kWh/km) (%) power(kW) power(kW) (%) Tesla Model S 85 0.177 86.4 3.7 44 96.16 capacity cost efficiency charging charging Efficiency Chevrolet Bolt 60 0.187 Nissan Leaf 24 0.212 (kWh) (kWh/km) (%) power(kW) power(kW) (%) Nissan Leaf 24 0.212 Chevrolet Bolt 60 0.187 (kWh/km) (%)86.4 power(kW) power(kW) (%)96.16 Tesla Model S (kWh) 85 0.177 3.7 44 Chevrolet Bolt 0.187 Tesla Model S 60 85 0.177 86.4 3.7 44 96.16 Chevrolet Bolt 60 0.187 Nissan Leaf 24 0.212 3.2 TheModel prediction of battery’s SOC Tesla S 85 0.177 86.4 3.7 44 96.16 Nissan Leaf 24 0.212 3.2 The prediction of battery’s SOC Tesla 0.177 86.4 3.7 44 96.16 NissanModel Leaf S 85 24 0.212 Nissan Leaf 24 0.212 3.2The Thekey prediction of battery’s SOC to determine the value of V2G potential capacity is precise of battery’s SOC. Driving patterns, to determine the value of V2G potential capacity is precise of battery’s SOC. Driving patterns, 3.2The Thekey prediction of battery’s SOC charging process and discharging process are related to this parameter. 3.2 The prediction of discharging battery’s SOC charging process and process are related to this isparameter. 3.2 The prediction of battery’s SOC The to determine value V2G potential capacity The key relation between the SOC and of trip distance is given as: precise of battery’s SOC. Driving patterns, relation between SOC and trip distance is given as: The key to determine the value of V2G potential capacity precise of battery’s SOC. Driving patterns, charging process and discharging process are related to this is SOC=SOC -D /C (7)SOC. Driving patterns, 0 n The key to determine the value of V2G potential capacity isparameter. precise of battery’s SOC=SOC /C (7)SOC. Driving patterns, charging process and discharging process are related to this parameter. 0-D nthe The key to determine value of V2G potential capacity is precise of battery’s relation between SOC and trip distance is given as: where SOC0 is the original SOC before driving. D is the trip distance. E is the power consumption n charging process and discharging process are related to as: this parameter. where SOC0 is the original SOC before driving. D the trip distance. E is the power consumption The relation between SOC and trip distance is given n is charging process and discharging process are related to this parameter. -D /C (7) SOC=SOC 0 n perThe kilometer driving and C is the battery capacity. relation between SOC and trip distance is given as: perThe kilometer driving and C is the battery capacity. SOC=SOC (7) 0-D n/C relation between SOC and trip distance is given as: triparticle: distance. E is power consumption where SOC=SOC SOC0 is the original SOC before driving. Dn isinthe (7)the There are three kind of charging patterns referred this uncontrolled charging, rapidly 0-Dn/C where SOC0 is the original SOC before driving. Dn isinthe triparticle: distance. E is the power consumption are three kind of charging patterns referred this uncontrolled charging, rapidly SOC=SOC (7) perThere kilometer driving and C is the battery capacity. 0-D n/C where SOC0 is the original SOC before driving. D is the trip distance. E is the power consumption n charging and V2G-smart charging. The probability of charging under different SOC (p(SOC)), the SOC per kilometer driving and C is the battery capacity. where SOC0 is the original SOC before driving. D is the trip distance. E is the power consumption charging and charging. Thepatterns probability of ncharging different SOC (p(SOC)), SOC are V2G-smart three kind charging referred in this under article: uncontrolled charging,the rapidly perThere kilometer driving andof C is the battery capacity. three kind of charging patterns referred in this article: uncontrolled charging, rapidly at There the endare of charging events(SOC ), the SOC needed for next journey(SOCj), the charging event start per kilometer driving and C is the battery capacity. E at There the endand of charging events(SOC ), the SOC referred needed for journey(SOCj), the charging event start are three kind of charging patterns in next this under article: uncontrolled charging, rapidly charging V2G-smart charging. probability of charging different SOC (p(SOC)), the SOC EThe There are three kindtime of charging referred in state this under article: uncontrolled charging, rapidly charging charging. Thepatterns probability of charging different SOC the SOC time (Ts),and theV2G-smart duration of charging event (tc) and the stay time (ts) are the(p(SOC)), core variables for time the duration events(SOC time of charging event and the next stateunder stay time (ts) are core variables for charging V2G-smart charging. probability of charging different SOC (p(SOC)), the SOC ), the SOC (tc) needed for journey(SOCj), thethe charging event start at the(Ts), endand of charging EThe and V2G-smart charging. probability ofor charging different SOC (p(SOC)), the at the endpatterns. of charging events(SOC ), the SOC needed for next under journey(SOCj), the charging event start EThe charging When the EVA gets into “Outside” “Athome” state, the charging algorithm willSOC run charging When the of EVA gets into “Outside” “Athome” state, charging algorithm willstart run at the(Ts), endpatterns. of charging events(SOC SOC needed next thethe charging event time the duration time charging event (tc) andorfor the statejourney(SOCj), stay timethe (ts) are core variables for E), the at end the of charging ), theevent SOC needed for journey(SOCj), thethe charging event start time (Ts), duration events(SOC time of The charging (tc) and the next statedescribed stay timein(ts) are core variables for Echarging forthe achieving charging events. pattern is detailed [13]. for achieving The charging pattern isordetailed in(ts) [13]. time (Ts),patterns. the charging duration time charging event (tc) and the statedescribed stay timethe are the algorithm core variables for charging Whenevents. the of EVA gets into “Outside” “Athome” state, charging will run time (Ts),patterns. the𝐷𝐷𝐷𝐷/2𝑁𝑁𝑁𝑁 duration charging event (tc) andorthe state stay timethe (ts) are the algorithm core variables for charging Whentime the of EVA gets into “Outside” “Athome” state, charging will run 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑈𝑈𝑈𝑈𝑈𝑈/𝑅𝑅𝑅𝑅𝑅𝑅 charging patterns. Whenevents. the EVA into “Outside” “Athome” state, the charging algorithm will run for𝑆𝑆𝑆𝑆𝑆𝑆 achieving charging Thegets charging pattern isordetailed described in [13]. 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑈𝑈𝑈𝑈𝑈𝑈/𝑅𝑅𝑅𝑅𝑅𝑅 (8) 𝑗𝑗 = { 𝐷𝐷𝐷𝐷/2𝑁𝑁𝑁𝑁 𝑆𝑆𝑆𝑆𝑆𝑆 = { (8) 𝐷𝐷𝐷𝐷/𝐶𝐶 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑆𝑆𝑆𝑆𝑆𝑆 charging Whenevents. the EVA into “Outside” “Athome” state, the charging algorithm will run for achieving charging Thegets charging pattern isordetailed described in [13]. 𝑗𝑗 patterns. 𝐷𝐷𝐷𝐷/𝐶𝐶 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 for achieving charging events. The charging pattern is detailed described in𝑆𝑆𝑆𝑆𝑆𝑆 [13]. 𝐷𝐷𝐷𝐷/2𝑁𝑁𝑁𝑁 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑈𝑈𝑈𝑈𝑈𝑈/𝑅𝑅𝑅𝑅𝑅𝑅 for𝑆𝑆𝑆𝑆𝑆𝑆 achieving charging events. The charging pattern is detailed described in [13]. = { (8) 𝐷𝐷𝐷𝐷/2𝑁𝑁𝑁𝑁 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑈𝑈𝑈𝑈𝑈𝑈/𝑅𝑅𝑅𝑅𝑅𝑅 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 ) , 100%) 𝑗𝑗 = 𝑃𝑃𝑃𝑃𝑃𝑃(max(𝑆𝑆𝑆𝑆𝑆𝑆, 𝐷𝐷𝐷𝐷/𝐶𝐶 𝑆𝑆𝑆𝑆𝑆𝑆(9) = {𝑃𝑃𝑃𝑃𝑃𝑃(max(𝑆𝑆𝑆𝑆𝑆𝑆, 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 ) , 100%) (9) 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗𝐸𝐸𝐸𝐸 = (8) 𝐷𝐷𝐷𝐷/2𝑁𝑁𝑁𝑁 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑈𝑈𝑈𝑈𝑈𝑈/𝑅𝑅𝑅𝑅𝑅𝑅 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 =𝑡𝑡 {𝐷𝐷𝐷𝐷/𝐶𝐶 (8) 𝐷𝐷𝐷𝐷/2𝑁𝑁𝑁𝑁 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑈𝑈𝑈𝑈𝑈𝑈/𝑅𝑅𝑅𝑅𝑅𝑅 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝐷𝐷𝐷𝐷/𝐶𝐶 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑆𝑆𝑆𝑆𝑆𝑆(9) 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗𝐸𝐸 = = 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 ) , 100%) {𝑃𝑃𝑃𝑃𝑃𝑃(max(𝑆𝑆𝑆𝑆𝑆𝑆, (8) 𝑡𝑡𝑜𝑜𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑜𝑜 𝐷𝐷𝐷𝐷/𝐶𝐶 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 ) , 100%) 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑇𝑇𝐵𝐵(9) (10) 𝑡𝑡𝑠𝑠 =𝐸𝐸{=𝑡𝑡𝑡𝑡𝑝𝑝𝑃𝑃𝑃𝑃𝑃𝑃(max(𝑆𝑆𝑆𝑆𝑆𝑆, 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑇𝑇𝐵𝐵(9) (10) 𝑡𝑡𝑠𝑠 =𝐸𝐸{= 𝑆𝑆𝑆𝑆𝑆𝑆 𝑝𝑝𝑃𝑃𝑃𝑃𝑃𝑃(max(𝑆𝑆𝑆𝑆𝑆𝑆, 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 ) , 100%) 𝑡𝑡𝑜𝑜𝑜𝑜𝑜𝑜 𝑇𝑇𝑃𝑃𝑃𝑃𝑃𝑃(max(𝑆𝑆𝑆𝑆𝑆𝑆, 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑎𝑎𝑎𝑎 𝑇𝑇𝐵𝐵(9) 𝑆𝑆𝑆𝑆𝑆𝑆𝐸𝐸 = 𝑆𝑆𝑆𝑆𝑆𝑆𝑗𝑗 ) , 100%) 1 + 24 − 𝑇𝑇𝐵𝐵 𝑡𝑡𝑡𝑡𝑜𝑜𝑜𝑜𝑜𝑜 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑇𝑇 + 24 − 𝑇𝑇 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎 𝑇𝑇 𝐵𝐵 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑇𝑇𝐵𝐵𝐵𝐵 (10) 𝑡𝑡𝑠𝑠 = {𝑡𝑡𝑜𝑜𝑜𝑜𝑜𝑜 𝑝𝑝1 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑡𝑡𝑝𝑝 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑇𝑇𝐵𝐵 𝑡𝑡𝑠𝑠 = {𝑡𝑡(𝑆𝑆𝑂𝑂𝐶𝐶 (10) 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑒𝑒 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑜𝑜 𝐸𝐸 𝐻𝐻 𝑇𝑇 + 24 − 𝑇𝑇 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎 𝑇𝑇 𝐵𝐵 𝑡𝑡𝑝𝑝1 𝐸𝐸 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑇𝑇𝐵𝐵 𝑡𝑡𝑡𝑡𝑠𝑠𝑐𝑐 = (10) (11) 𝑒𝑒 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 = {{(𝑆𝑆𝑂𝑂𝐶𝐶 + 24 − 𝑇𝑇𝐵𝐵 𝐻𝐻 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎 𝑇𝑇𝑇𝑇𝐵𝐵𝐵𝐵𝐵𝐵 𝑡𝑡𝑇𝑇 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = {{(𝑆𝑆𝑆𝑆𝑆𝑆 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑡𝑡𝑡𝑡𝑠𝑠𝑐𝑐 = (10) (11) 𝑝𝑝1 𝐸𝐸 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑅𝑅 𝑒𝑒 𝑇𝑇 + 24 − 𝑇𝑇 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎 𝑇𝑇 (𝑆𝑆𝑆𝑆𝑆𝑆 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑒𝑒 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 1 𝐸𝐸 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝐵𝐵 𝐵𝐵 𝑅𝑅 𝑒𝑒 (𝑆𝑆𝑂𝑂𝐶𝐶 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝐸𝐸 𝐻𝐻 𝑇𝑇 + 24 − 𝑇𝑇 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎 𝑇𝑇 1 𝐵𝐵 EVs in 𝐵𝐵V2G𝐻𝐻 activity as whole, the discharge patterns𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 are decided by the(11) coordination {(𝑆𝑆𝑂𝑂𝐶𝐶 𝑡𝑡𝑐𝑐 = participant 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑒𝑒 EVs participant in V2G𝑅𝑅 𝑒𝑒activity as whole, the discharge patterns𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 are decided by the(11) coordination − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 (𝑆𝑆𝑆𝑆𝑆𝑆𝐸𝐸𝐸𝐸 − 𝑡𝑡𝑐𝑐 = agent {(𝑆𝑆𝑂𝑂𝐶𝐶 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑒𝑒 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 𝐸𝐸 the 𝐻𝐻𝑒𝑒 control in V2G-equipped energy system. The dispatch progress is not for a single EV agent, it (𝑆𝑆𝑆𝑆𝑆𝑆 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑡𝑡𝑐𝑐 = agent {(𝑆𝑆𝑂𝑂𝐶𝐶in𝐸𝐸𝐸𝐸 the (11) 𝑅𝑅 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑒𝑒 𝐴𝐴𝐴𝐴ℎ𝑜𝑜𝑜𝑜𝑜𝑜 control V2G-equipped energy system. The dispatch progress is not for a single EV agent, it 𝐻𝐻𝑒𝑒 (𝑆𝑆𝑆𝑆𝑆𝑆𝐸𝐸 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 EVs inEV. V2G as whole, are decided bystate, the(11) coordination 𝑡𝑡𝑐𝑐 the = participant {aggregated 𝑅𝑅 activity is for When EV finish its lastthe timedischarge “driving”patterns and turn to “Athome” with its SOC (𝑆𝑆𝑆𝑆𝑆𝑆 − 𝑆𝑆𝑆𝑆𝑆𝑆)/𝑃𝑃 𝑒𝑒 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 is for the aggregated EV. When EV finish its last time “driving” and turn to “Athome” state, with its SOC EVs participant in V2G activity as whole, the discharge patterns are decided by the coordination control in𝐸𝐸 the V2G-equipped energy system. dispatch progress isdecided notwill for be abysingle EV agent, it andEVs the agent duration ofin stay ts𝑅𝑅meet the requirements, EVare agent connected to the participant V2G activity ascorresponding whole, the The discharge patterns the coordination and the duration of stay t meet the corresponding requirements, EV agent will be connected toSOC the control agent in the V2G-equipped energy system. The dispatch progress is not for a single EV agent, it s EVs participant in V2G activity as whole, the discharge patterns are decided by the coordination is for the aggregated EV. When EV finish its last time “driving” and turn to “Athome” state, with its aggregator. The optimal agent will energy decide system. which EV isdispatch the needed one according available SOC of control agent in the V2G-equipped The progress is“Athome” not for ato single EV agent, it aggregator. The optimal agent will decide which EV is the needed one according to available SOC of is for the aggregated EV. When EV finish its last time “driving” and turn to state, with its SOC control agent in energy system. The dispatch progress is“Athome” not for be a changes single EV agent, it the corresponding requirements, EVvalue agent connected totime. the and the duration of V2G-equipped stayWhen tprocess, s meet EV’s battery. In the the V2G EV discharges to the grid and the of will SOC over is for the aggregated EV. EV finish its last time “driving” and turn to state, with its SOC EV’s battery. In optimal the EV discharges to the gridneeded and the value of will SOCbe changes over and the duration of V2G stayWhen tprocess, the corresponding requirements, EV agent connected totime. the s meet is for the aggregated EV. EV finish its last time “driving” and turn to “Athome” state, with its SOC aggregator. The agent will decide which EV is the one according to available SOC of For battery life preservation, the the finalcorresponding SOC after discharging should not be will less be than 20%[14].toAnd and the duration of stayagent ts meet requirements, EV agent connected the For battery life preservation, the final SOC afterEV should not be less be than 20%[14]. And aggregator. The willthe decide which is the needed one according to available SOC of and the duration of stayanxiety, tprocess, corresponding requirements, EV agent connected totime. the EV’s battery. In optimal the V2G EV discharges todischarging the grid and the value of will SOC over s meet remaining considering users’ range capacity would well beyond the value oftochanges SOC which merely aggregator. The optimal agent will decide which EV is the needed one according available SOC of considering users’ range anxiety, remaining capacity would well beyond the value of SOC which merely EV’s battery. In the V2G process, EV discharges to the grid and the value of SOC changes over time. aggregator. The optimal agent will decide which is the one according to available SOC of For life preservation, final SOC afterEV discharging should not be lessmore than 20%[14]. And meetbattery the driving needs. In thisthe paper, the remaining SOC isneeded defined that should than 30%. The EV’s battery. In the V2G process, EV discharges to the grid and the value of SOC changes over time. meet the driving needs. Inprocess, thisthe paper, the remaining SOC is and defined that should more than 30%. The For battery life preservation, final SOC aftertodischarging should not be lessof than 20%[14]. And EV’s battery. In the V2G EV discharges the grid the value of SOC changes over time. considering users’ range anxiety, remaining capacity would well beyond the value SOC which merely requirements arepreservation, listed as below: For battery users’ life theremaining final SOCcapacity after discharging should not be lessofthan 20%[14]. And requirements arepreservation, listed considering rangeasanxiety, would beyondthat theshould value SOCthan which merely For battery life finalthe SOC after discharging should not be lessof than 20%[14]. And meet the>driving needs. Inbelow: thisthe paper, remaining SOC well is defined more 30%. The SOC 𝑆𝑆𝑆𝑆𝑆𝑆 30% anxiety, (12) considering users’ remaining capacity would well beyond the value SOC which merely 𝑗𝑗 + range SOC 𝑆𝑆𝑆𝑆𝑆𝑆 30%asanxiety, (12)of meet the>driving needs. Inbelow: this paper, the remaining SOC well is defined that should more than 30%. The 𝑗𝑗 + considering users’ range remaining capacity would beyond the value SOC which merely requirements are listed 𝑡𝑡𝑠𝑠 the > 𝑡𝑡𝑖𝑖driving needs. In this paper, the remaining SOC is defined that should (13) more than 30%. The meet 𝑡𝑡𝑠𝑠 the >> 𝑡𝑡𝑖𝑖driving (13) requirements are+listed asInbelow: meet needs. this the remaining SOC is defined that should 30% (12) more than 30%. The 𝑆𝑆𝑆𝑆𝑆𝑆 the SOC achievable SOC of each EV paper, is calculated as: 𝑗𝑗 listed requirements are as below: the achievable SOC of each EV is calculated as: SOC > 𝑆𝑆𝑆𝑆𝑆𝑆 + 30% (12) 𝑗𝑗 listed as below: requirements are > 𝑡𝑡 (13) 𝑡𝑡SOC 𝑠𝑠 𝑖𝑖 SOCa=SOC-SOCj-30% (14) > (12) 𝑗𝑗 + 30% SOCa=SOC-SOCj-30% (14) 𝑡𝑡𝑖𝑖 𝑆𝑆𝑆𝑆𝑆𝑆 (13) 𝑠𝑠 > > 𝑆𝑆𝑆𝑆𝑆𝑆SOC (12) the 𝑡𝑡SOC achievable of each EV is calculated as: 𝑗𝑗 + 30% 𝑡𝑡 > 𝑡𝑡 (13) 𝑠𝑠 𝑖𝑖 the 𝑡𝑡achievable SOC of each EV is calculated as: > 𝑡𝑡 (13) SOCa=SOC-SOCj-30% (14) 𝑠𝑠 𝑖𝑖 the achievable SOC of each EV is calculated as: SOCa=SOC-SOCj-30% (14) the achievable SOC of each EV is calculated as: 3.3 SOCa=SOC-SOCj-30% The calculation of V2G capacity (14) 3.3 SOCa=SOC-SOCj-30% The calculation of V2G capacity (14) 3.3 The calculation of V2G capacity This is how to define V2G capacity: the energy stored in arranged vehicles, devided by the time it is ThisThe is calculation how to define V2Gcapacity capacity: the energy stored in arranged vehicles, devided by the time it is 3.3 of V2G drawn. specifically, limit is the onboard energy storage at this moment less needed for the next 3.3 TheMore calculation of V2Gits capacity drawn. More specifically, its limit is thethe onboard at thisvehicles, momentdevided less needed fortime the next 3.3 of V2G ThisThe is calculation how to define V2Gcapacity capacity: energyenergy stored storage in arranged by the it is This is how to define V2G capacity: the energy stored in arranged vehicles, devided by the time it is

Charging Charging Charging Charging demand/kW demand/kW demand/kW demand/kW

Charging Charging Charging Charging demand/kW demand/kW demand/kW demand/kW



Author name / Energy Procedia 00 (2018) 000–000 Author name / Energy Procedia 00 (2018) 000–000 name /Liu Energy 00 (2018) Yiling et al.times / Procedia Energy 152 (2018) 630–635 stored energy to the trip and the requirement ofAuthor remaining capacity, theProcedia efficiency of000–000 converting Author name / Energy Procedia 00 (2018) 000–000 trip and the requirement of remaining capacity, times the efficiency of converting stored energy to the power grid, all divided by the duration of time the energy is dispatched. It is calculated in Eq.1. trip andgrid, the all requirement of remaining capacity, times the efficiency of converting stored energy to the power divided by the duration of time the energy is dispatched. It is calculated in Eq.1. 𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ×(𝑆𝑆𝑆𝑆𝑆𝑆of 𝑡𝑡 −𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆−30%)×𝜂𝜂 𝑖𝑖𝑖𝑖𝑖𝑖 trip𝑃𝑃 and the=requirement remaining capacity, times the efficiency of converting stored energy to the (15) power grid, all divided by the duration of time the energy is dispatched. It is calculated in Eq.1. 𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ×(𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡𝑡𝑡−𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆−30%)×𝜂𝜂 𝑖𝑖𝑖𝑖𝑖𝑖time the energy is dispatched. It is calculated in Eq.1. 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 duration of power grid,=all divided by the 𝑃𝑃𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 (15) 𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ×(𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡𝑡𝑡𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 −𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆−30%)×𝜂𝜂𝑖𝑖𝑖𝑖𝑖𝑖 𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ×(𝑆𝑆𝑆𝑆𝑆𝑆 −𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆−30%)×𝜂𝜂 𝑃𝑃 (15) 𝑡𝑡maximum 𝑖𝑖𝑖𝑖𝑖𝑖 𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑃𝑃= where is the power from each EV participanted in dispatching in kW, is the 𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑡𝑡𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑃𝑃𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = (15) 𝑡𝑡 where 𝑃𝑃 is the maximum power from each EV participanted in dispatching is the 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 electrical conversion efficiency of the DC to AC inverter (dimensionless) and tdisp is timeinthekW, vehicle’s where 𝑃𝑃 is the maximum power from each EV participanted in dispatching in kW, is the 𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 electrical conversion of thepower DC tofrom ACmust inverter anddispatching tof vehicle’s disp is timeinthe where 𝑃𝑃𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is efficiency the maximum eachless EV(dimensionless) participanted in kW, is the stored energy is dispatched in hours. 𝑃𝑃𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 than the maximum battery’s discharging electrical conversion efficiency of the DC to AC inverter (dimensionless) and t is time the vehicle’s disp stored is dispatched in hours. 𝑃𝑃𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 less (dimensionless) than the maximum discharging electrical conversion efficiency of the DC to ACmust inverter and tof is time the vehicle’s dispbattery’s power, energy usually Pd,max =0.5C=30kW. stored energy is dispatched in hours. 𝑃𝑃𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 must less than the maximum of battery’s discharging power, usually P =0.5C=30kW. stored energy is d,max dispatched hours. 𝑃𝑃𝑣𝑣𝑣𝑣ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 mustofless than the maximum The achievable capacity of in V2G system is the sum all connected EVs’ Pvehicleofatbattery’s one time.discharging The factors power, usually Pd,max =0.5C=30kW. The achievable capacity of V2G system is the sum of all connected EVs’ P at one time. The factors vehicle power, usually P =0.5C=30kW. d,max of V2G’s achievable power capacity are: the number of EVs and battery capacity. The achievable capacity of V2G system is the sum of all connected EVs’ P vehicle at one time. The factors of The V2G’s achievable powerofcapacity are: the number ofall EVs and battery achievable capacity V2G system is the sum of connected EVs’capacity. Pvehicle one time. So, in this study, four different penetration rates have been considered: 10%,at30%, 50%The andfactors 90%, of So, V2G’s achievable power capacity are: the number of EVs and battery capacity. in this study, four different penetration rates have been considered: 10%, 30%, 50% and 90%, of V2G’s achievable power are: the are number of EVs andpaper, batteryrespectively capacity. are 24kWh, 60kWh, respectively. And three typescapacity of EV batteries involved in this So, in this study, four different penetration rates have been considered: 10%, 30%, 50% and 90%, respectively. types of EV batteries arerates involved inbeen this considered: paper, respectively are 24kWh, 60kWh, So, inAlso, thisAnd study, four different penetration have 10%, 30%, 50% and 90%, 85kWh. we three simulated the smart charging patterns without V2G respectively. And three types of EV batteries are involved in this paper, respectively are 24kWh, 60kWh, 85kWh. Also,And we three simulated smart charging respectively. typesthe of EV batteries arepatterns involvedwithout in this V2G paper, respectively are 24kWh, 60kWh, 85kWh. Also, wediscussions simulated the smart charging patterns without V2G 4. Results and 85kWh. Also, we simulated the smart charging patterns without V2G 4. Results and discussions 4. InResults and discussions this section, simulation results for the case under different EV penetration and different batteries, 4. Results and discussions In this section, simulation results for the case under different EV penetration and different batteries, are reported. In this section, simulation results for charging the case under penetration andpenetration different batteries, areThe reported. charging demand of smart patterndifferent and V2GEV pattern under EV of 10%, In this section,power simulation results for the case under different EV penetration and different batteries, are reported. The charging power demand of smart charging pattern and V2G pattern under EV penetration of 10%, 30%, 50% are shown in Fig.3. and Fig.4. In smart charging patterns, the charging time are concentrated are reported. The charging power demand of smart charging pattern and V2G pattern under EV penetration of 10%, 30%, 50% are shown in Fig.3. and Fig.4. In smart charging patterns, the charging time are concentrated at the timepower of griddemand load, asofthe smart charging algorithm. The pattern charging power V2G patterns have Thevalley charging smart charging pattern and V2G under EVinpenetration of 10%, 30%, 50% are shown in Fig.3. and Fig.4. In smart charging patterns, the charging time are concentrated at the valley time of grid load, as the smart charging algorithm. The charging power in V2G patterns have the same tendency as in smart charging patterns. However, the total demand are totally different 30%, 50% are shown in Fig.3. and Fig.4. In smart charging patterns, the charging time are concentrated at the valley time of grid load, as the smart charging algorithm. The charging power in V2G patterns the same tendency as in smart charging patterns. However, the total demand are totally different from undertime the same EV penetration. Thecharging total demand dramatically increases under V2G patterns patterns,have that at theitvalley of grid load, as the smart algorithm. The charging power in V2G have the same tendency as in smart charging patterns. However, the total charging demand are totally different from it under the same EV penetration. The total demand dramatically increases under V2G patterns, that is because EV need to finish electricity storage during the valley period, and then discharge to supply the same tendency as in smart charging patterns. However, the total charging demand are totally different from it under same penetration. The total demand dramatically under V2G patterns, that is because EVthe need to EV finish electricity storage during valley and then to and supply energy to the power the power is at thethe peak. As period, theincreases efficiency of discharge charging η the from it under the samegrid EV when penetration. The load total demand dramatically increases under V2G patterns, that is because EV need to finish electricity storage during the valley period, and then discharge to supply energy to the power grid when the power load is at the peak. As the efficiency of charging η and the , the energy waste is inevitable. As a result, electricity company electricity conversion efficiency 𝜂𝜂 𝑖𝑖𝑖𝑖𝑖𝑖 is because EV need to finish electricity storage during the valley period, and then discharge to supply energy to the power grid when the power load is at the peak. As the efficiency of charging η and electricity conversion efficiency 𝜂𝜂 , the energy waste is inevitable. As a result, electricity company would price grid difference the is profits both As users itself. of charging η and the 𝑖𝑖𝑖𝑖𝑖𝑖 energy raise to thethepower when to theguarantee power load at theofpeak. theand efficiency the electricity efficiencyto𝜂𝜂guarantee , the energy waste is inevitable. As a result, electricity company would raiseconversion the price400 difference the profits of both users and itself. electricity conversion efficiency 𝜂𝜂𝑖𝑖𝑖𝑖𝑖𝑖 company 𝑖𝑖𝑖𝑖𝑖𝑖 , the energy waste is inevitable. As a result, electricity 10% would raise the price400 difference to guarantee the profits of both users and itself. would raise the price difference to guarantee the profits of both users and itself. 10% 300 400 400 300 200 300 300 200 100 200 200 100 0 100 100 0 0 0

1500 1500 1500 1500 1000 1000 1000 1000 500 500 500 500 0 0 0 0

30% 10% 30% 50% 10% 30% 50% 30% 50% 50%

1 1 1 1

1 1 1 1

2 2 2 2

2 2 2 2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 3 4 5 6 7 8 9 10Time/hour 11 12 13 14 15 16 17 18 19 20 21 22 23 24 3Fig.4 1.5Charging 6 7 8demand 9 10Time/hour 11 12 13 14 15 16 17pattern 18 19 20 21 22 23 24 under smart charging 3 4 5 6 7 8 9 10Time/hour 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Fig. 1. Charging demandTime/hour under smart charging pattern Fig. 1. Charging demand under smart charging pattern Fig. 1. Charging demand under smart charging pattern

10% 10% 30% 10% 30% 50% 10% 30% 50% 30% 50% 50%

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 3 4 5 6 7 8 9 10Time/hour 11 12 13 14 15 16 17 18 19 20 21 22 23 24 3 Fig. 4 2. 5 Charging 6 7 8 demand 9 10Time/hour 11under 12 13 14 15 16 17 pattern 18 19 20 21 22 23 24 charging 3 4 5 6 7 8 9 10Time/hour 11 12 13V2G 14 15 16 17 18 19 20 21 22 23 24

Fig. 2.inCharging demand under V2G charging pattern Time/hour The number of EVs participanted the energy dispatch determines the real-time V2G output and Fig. 2. Charging demand under V2G charging pattern Theachievable number ofcapacity, EVs participanted thebetween energy dispatch determines the real-time The V2GV2G output and V2G the relationship the twoV2G numbers complicated. output Fig. 2.inCharging demand under chargingis pattern Theachievable number ofcapacity, EVs participanted in the energy dispatch determines the real-time V2G output and V2G the relationship between therate twoisnumbers is complicated. The V2G output power and capacity under different EV penetration shown in Fig.5. We can see that when The number of EVs participanted in the energy dispatch determines the real-time V2G output and V2G achievable capacity, the relationship betweenisthe twoto isoutput. complicated. The power and rate capacity under different EV capacity penetration rate isnumbers shown in Fig.5.When We can seeV2G that output when penetration is 10% and 30%, the V2G equal the V2G the penetration are V2G achievable capacity, the relationship between the two numbers is complicated. The V2G output power and rate capacity under different EV capacity penetration rate to is the shown Fig.5.When We can see that when penetration is 10% and 30%, the V2G is equal V2Ginoutput. the penetration are

633

Author name / Energy Procedia 00 (2018) 000–000 Author name / Energy Procedia 00 (2018) 000–000 Author name / Energy Procedia 00 (2018) 000–000 incresed to 50%, there are difference appeared, the spare V2G capacity occurs during 7:00 am to 12:00 Author name /Liu Energy 00 capacity (2018) 000–000 634 incresed etisal. / Procedia Energy Procedia 152 (2018) 630–635 to V2G’s 50%, there aretodifference appeared, the sparethan V2G occurs during am to am. So the ability supply name toYiling the /grid higher the(2018) real situation. As wee 7:00 can see, the12:00 total Author Energy Procedia 00 000–000 incresed to 50%, there are difference appeared, the spare V2G capacity occurs As during am to am. So theEV V2G’s ability to supply to the is higher thanThe the squre real situation. wee 7:00 can see, the12:00 total electricity aggregator could supply is grid almost the same. ennergy storage if not consumed, am. Sosuply the V2G’s ability to supply to appeared, the iswill higher than the real situation. As wee can see, the12:00 total incresed toEV 50%, there are difference the spare V2G capacity occurs during 7:00 am to electricity aggregator could supply is grid almost theeffect same. The squre ennergy storage if not consumed, it will to the grid later., the consumption the capacity at the rest of time. To investigate incresed toEV 50%, there arecould difference appeared, the spare V2G capacity occurs storage during 7:00 am to 12:00 electricity aggregator supply is almost the same. The squre ennergy if not consumed, am. So the V2G’s ability to supply to the grid is higher than the real situation. As wee can see, the total it will suply to the grid later.,tothe consumption will effect the capacity at potential the rest ofcapacity time. Toisinvestigate the V2G capacity meet the concept of V2G proposed, am. Sosuply thepotential V2G’s ability to supply tothe thegrid gridneed, iswill higher than the real situation. Asofwee canTosee, the total it will to the grid later., the consumption effect the capacity at the rest time. investigate electricity EV aggregator could supply is almost the same. The squre ennergy storage if not consumed, the V2G potential to meet arranged the grid need, thetheconcept of V2G potential capacity is proposed, which namely no capacity EVs have before given moment. There are two factors being electricity EV aggregator couldbeen supply almost thethe same. The squre ennergy storage if not consumed, the V2G potential capacity meet theisgrid need, of V2G capacity isinvestigate proposed, it will suply to the consumption will effect the capacity at potential the rest of time. which namely no grid EVs later., havetothe been arranged before theconcept given moment. There are two To factors being considered. it will suply to the consumptionbefore will effect given the capacity at the rest of time. investigate which namely no grid EVs later., havetothe been moment. There are two To factors being the V2G potential capacity meet arranged the grid need, thetheconcept of V2G potential capacity is proposed, considered. the V2G potential capacity to meet the grid need, the concept of V2G potential capacity is proposed, considered. which namely no EVs have been arranged before the given moment. There are two factors being which namely no EVs have been arranged before the given moment. There are two factors being considered. considered.

Fig. 3. V2G output and capacity under different penetration

V2G V2Gpotential V2G potential V2G V2G potential potential potential capacity/kW capacity/kW capacity/kW capacity/kW capacity/kW

Fig. the 3. V2G output and capacity The five lines in Fig.6. have same tendency, theunder valuedifferent underpenetration different penetration rate are quite Fig. the 3. V2G output and capacity under different penetration The five lines in Fig.6. have same tendency, the value underthe different penetration are quite distinct. It is because the number of EV are largely increase from penetration of 10%rate to 90%, the The five lines in Fig.6. have the same tendency, the value under different penetration rate are quite Fig. 3. V2G output and capacity under different penetration distinct. It is because the number of EV are largely increase from the penetration of 10% to 90%, the total storage capacity added,Fig. of 3. course. When battery size change from 24 kWh to 60 kWh, there is V2G output and capacity under different penetration distinct. It is because the number of EV are largely increase from the penetration of 10% to 90%, the The five lines in Fig.6. have the same tendency, the value under different penetration rate are quite total storage capacity added, of course. When battery size change from 24 kWh to 60 kWh, there is obvious increasing, the maximum varied from 1650.2 kW to 1868.5 kW, increased by 13.22%. However, The five lines in Fig.6. have the sameWhen tendency, the size valuechange under from different penetration rate are quite total storage capacity added, of course. battery 24 kWh to 60 kWh, there is distinct. It is because the number of EV are largely increase from the penetration of 10% to 90%, the obvious increasing, the maximum varied from 1650.2 kW to 1868.5 kW, increased by 13.22%. However, difference 60the kWh and 85ofkWh is slightly. Most points of the the penetration two lines areofcoincident, during distinct. It between is because number EV from are largely increase from 10% to However, 90%, the obvious increasing, the maximum varied 1650.2 kW to 1868.5 kW, increased by 13.22%. total storage capacity added, of course. When battery size change from 24 kWh to 60 kWh, there is difference between 60 kWh and 85 kWh is slightly. Most points of the two lines are coincident, during 8:00 am to 14:00 pm added, they have divergence. Inbattery general, are from no significant total storage capacity of course. When sizethere change 24lines kWhare todifference 60 kWh, between there is difference 60 and kWhfrom is slightly. Most of kW, the coincident, during obvious the kWh maximum varied 1650.2 kW topoints 1868.5 increased bydifference 13.22%. However, 8:00 amincreasing, tobetween 14:00 pm they have85 divergence. In general, there are no two significant between different type of battery. obvious increasing, the maximum varied from 1650.2 kW to 1868.5 kW, increased by 13.22%. However, 8:00 am type tobetween 14:00 pm they and have85divergence. In general, there are no two significant difference 60 kWh kWh is slightly. Most points of the lines aredifference coincident,between during different of battery. difference between 60 kWh and 85 kWh is slightly. Most points of the two lines are coincident, during different of battery. 8:00 am type to 14:00 pm they have divergence. In general, there are no significant difference between 8:00 am2500 to 14:00 pm they have divergence. In general, there are no significant difference between 10%-24kWh 50%-24kWh 90%-24kWh different type of battery. different2500 type of battery. 10%-24kWh 50%-24kWh 90%-24kWh

2500 2000 2500 2000 2500 2000 1500 2000 1500 2000 1500 1000 1500 1000 1500 1000 500 1000 500 1000 500 0 500 0 500 0 0 0

90%-60kWh 10%-24kWh 90%-60kWh 10%-24kWh 90%-60kWh 10%-24kWh 90%-60kWh 90%-60kWh

1 1 1 1 1

2 2 2 2 2

3 3 3 3 3

4 4 4 4 4

5 5 5 5 5

6 6 6 6 6

7 7 7 7 7

8 8 8 8 8

9 9 9 9 9

10 10 10 10 10

11 12 13 14 11 12 13 14 11time/hour 12 13 14 time/hour 11time/hour 12 13 14 11 12 13 14

90%-85kWh 50%-24kWh 90%-85kWh 50%-24kWh 90%-85kWh 50%-24kWh 90%-85kWh 90%-85kWh

15 15 15 15 15

16 16 16 16 16

17 17 17 17 17

Fig. 4. V2G potential capacity under different cases time/hour time/hour Fig. 4. V2G potential capacity under different cases Fig. 4. V2G potential capacity under different cases

18 18 18 18 18

19 19 19 19 19

90%-24kWh 90%-24kWh 90%-24kWh

20 20 20 20 20

21 21 21 21 21

22 22 22 22 22

23 23 23 23 23

24 24 24 24 24

5. Conclusions Fig. 4. V2G potential capacity under different cases 5. Conclusions Fig. 4. V2G potential capacity under different cases 5. This Conclusions paper develops an agent-based model to calculate the V2G potential capacity and analyze the 5. This Conclusions paper develops an agent-based model to calculate the shows V2G potential capacity and analyze the related factors: EV penetration rate and battery size. The result that: 5. This Conclusions paper develops an agent-based model to calculate the V2G potential and analyze the related factors: EV penetration rate and battery size. The result shows that: 1. To supply electricity to the grid, EVs must charging more during the capacity valley period. When the related factors: EV penetration rate and battery size. The result shows that: This paper develops agent-based to calculate themore V2Gto potential and analyze 1. power To supply electricity topeak, the grid, EVs EVs must during the capacity valley When the demand isan at the the model spare ascharging an aggregator participant in theperiod. dispatch, release This paper develops an agent-based model to calculate themore V2Gduring potential capacity and analyze the 1. factors: To supply electricity to the grid, EVs must charging the valley period. When the related EV penetration rate and battery size. The result shows that: power demand is at the peak, the spare EVs as an aggregator to participant in the dispatch, release the stored electricity lessrate theand required part. related factors: EV penetration battery size. The result shows that: demand is at the the spare EVs an aggregator participant in theperiod. dispatch, release 1. power To electricity topeak, the EVspart. mustascharging more to during the valley When the the supply stored electricity less thegrid, required 1. To supply electricity to the grid, EVs must charging more during the valley period. When the



Yiling Liu et al. / Energy Procedia 152 (2018) 630–635 Author name / Energy Procedia 00 (2018) 000–000

2. 3.

V2G output is often less than the true capacity with high penetration, usually it is spare. The potential capacity does not consider the foregone condition, and guarantee the maximum capacity delivering to the grid. The higher penetration rate, the larger V2G potential capacity. The penetration rate has considerable influence on the potential capacity, however, the battery size does not. Slightly increase takes to the varied battery capacity.

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