電動汽車消費者的私人充電樁外文文獻

Pricing for private charging pile sharing considering EV consumers based on non cooperative game model Zhenli Zhao a b Lihui Zhanga b Meng Yanga b Jianxue Chaia b Songrui Lia b aSchool of Economics and Management Beijing China bBeijing Key Laboratory of New Energy and Low Carbon Development North China Electric Power University Changping Beijing 102206 China a r t i c l e i n f o Article history Received 12 September 2019 Received in revised form 4 January 2020 Accepted 6 January 2020 Available online 13 January 2020 Handling editor Dr Sandra Caeiro Keywords Charging price Private charging pile sharing service Consumers charging behavior Sharing rate Non cooperative game model a b s t r a c t The supply of public charging infrastructure is insuffi cient to meet the charging demand of a large number of electric vehicles EVs Private charging pile sharing is an emerging solution to alleviate this imbalance However a reasonable price for charging pile sharing has not yet been determined This study employs a non cooperative game model to determine a charging pile sharing price considering EV consumers charging behaviors First a multi logit model is constructed to measure the probability of EV consumers charging behavior choices Then a two matrix game model is established between the private charging pile sharing and public charging mode Using Beijing as a sample the sharing rate and price strategies of private and public charging piles are calculated based on the proposed game model The results show that the optimal sharing rate is 20 01 private charging pile sharing with 1 14 yuan kWh and 79 99 public charging with 1 7946 yuan kWh Sensitivity analysis shows that economics is the most sensitive factor affecting the charging price of private charging pile sharing Finally policy rec ommendations are outlined to improve the private charging pile sharing rate and service effi ciency to broaden the charging options for EV consumers reduce the construction of public charging piles and save the government subsidy 2020 Elsevier Ltd All rights reserved 1 Introduction 1 1 Background of private charging pile sharing service Increasing consumption of fossil fuels and climate change have become urgent global issues Adnan et al 2017a Adnan and M Vasant 2016 Previous research Adnan et al 2019 Rahman et al 2016 has shown that electric vehicles EV are an impor tant waytohelp reduce fossil fuel consumption and greenhouse gas emissions GHG The Chinese government regards the develop ment of EVs as a strategic objective and the country is projected to have fi ve million EVs in operation by 2020 Zhu et al 2017 As of 2018 the number of EVs in China had reached 2 61 million ac counting for 1 09 percent of total vehicles Among the various kinds of EVs the Blade Electric Vehicle BEV sales totaled 2 11 million making it one of the most popular China has become the world s largest electric vehicle market and sales of electric vehicles still continue to experience rapid growth Wang et al 2019a However as of the end of 2018 there were only 777 000 charging piles available as shown in Fig 1 among which only 30 000 were public charging facilities accounting for 38 96 De et al 2019 Tan et al 2019 The development and consumeradoption of electric vehicles have been hampered by the imbalance between the supply of and demand for public charging infrastructure CI Ou et al 2018 However it is not clear that the best way to resolve the imbal ance is to expand the scale of public charging facilities On the one hand the development of EVs and CI is still immature and in the beginning stages L Zhang et al 2019b Charging congestion during some periods was caused by the uneven distribution of public charging facilities and ineffective planning Adnan et al 2017b Additionally the availability of private charging piles is concentrated during the evenings while the piles sit mostly idle during the day thus resulting in a low utilization rate Patt et al 2019 To a certain extent the unbalanced utilization of charging resources and the low utilization rate of charging facilities have been caused by the inadequate management of CI Lopez Behar et al 2019 On the other hand it is unrealistic to expand the scale of public charging piles to meet increasing charging demand due to the shortage of land resources especially in large and Corresponding author School of Economics and management Beijing China E mail address 1172106006 Z Zhao Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage https doi org 10 1016 j jclepro 2020 120039 0959 6526 2020 Elsevier Ltd All rights reserved Journal of Cleaner Production 254 2020 120039 medium sized cities Gnann et al 2019 where parking space is a scarce resource The private charging pile sharing service mode which has introduced an IT enabled transportation sharing service has attracted increasing attention and achieved tremendous suc cess in easing the problem of imbalanced charging piles Plenter et al 2018 With the development of Information Technology IT the sharing economy is emerging at a historic moment some in dustries have launched brand new businesses related tothe sharing economy especially in transportation services i e EV sharing bike sharing and car sharing Ma et al 2019 Private charging pile sharing services emerged in 2014 in China Nengyuan 2017 Relying on IT private charging infrastructure owners can release the charging information location idle time charging sharing price charging pile standard etc to a sharing management plat form Plenter et al 2018 Based on time cost and location EV consumers can book a charging appointment online or by phone Generally since private charging facilities are located in residential areas charging prices and parking fees are lower than those for public charging stations Fotouhi et al 2019 Zhang et al 2018 Through private charging pile sharing the charging cost of EVs can be reduced and private owners and management platforms can share the charging benefi ts At the same time the range anxiety experienced by EV consumers can be alleviated through an expansion of private charging resources which can result in a win win situation for multiple parties Thus a sharing service is an effective business model to achieve sustainable value for EVs and charging infrastructure development The heart of private charging pile sharing service is the charging price as it determines the commercial viability and profi tability of the sharing model Establishing a reasonable charging price is extremely complex and has inspired in depth research by scholars Li and Ouyang 2011 calculated a charge price range from the perspectives of station operators and EV users while Pelzer et al 2014 proposed a price responsive charging and dispatching strategy to analyze the EV owners benefi ts A charge price model based on the system dynamic SD technique was established by Zhang et al 2018 and Hu et al 2016 designed a charge price mechanism to guide EV charging behavior and fi ll the load valley Moghaddam et al 2019 presented a coordinated dynamic pricing model to increase the temporal EV load shifting during evening peak hours and reduce overlaps between residential and charging station loads Y Zhang et al 2019 designed an optimal pricing scheme to improve the charging station service rates Limmer 2019 believed that dynamic pricing is an appropriate means for releasing the potential fl exibility from electric vehicle users A novel pricing and scheduling mechanism for charging discharging Nomenclature VkGeneralized charging cost of the k th charging mode bThe subscript b represents the private charging mode dThe subscript d represents the public charging mode TkThe utility value of the timeliness of the k th charging mode EkThe utility value of the economy of the k th charging mode CkThe utility value of the convenience of the k th charging mode DkThe utility value of the connectivity of the k th charging mode SkThe utility value of the safety of the k th charging mode qsThe weighted value of consumers preference for safety parameter qtThe weighted value of consumers preference for timeliness parameter qeThe weighted value of consumers preference for economy parameter qcThe weighted value of consumers preference for convenience parameter qdThe weighted value of consumers preference for connectivity parameter WThe battery energy of a standard EV tkCharging time of the k th charging mode PwkPower of charging pile of the k th charging mode VOTThe value of time GNPUrban per capita disposable income tAverage annual working hours pkCharging price of the k th charging mode ekParking fee of the k th charging mode Xk Pile fi nding time of the k th charging mode YkSettlement time of the k th charging mode dkThe walking distance from the destination to the k th charging point nWalking speed jSafety constant dk Safety coeffi cient of the k th charging mode UkThe utility function of the k th charging mode l Correction coeffi cient xkSharing rate of the k th charging mode QCharging power qkCharging power of the k th charging mode 4Gravity parameter gGravity parameters related to the charging price pCharging revenue cVariable costs vCharging utilization ratio POActual charging power per charging pile PCIAnnual available charging power per charging pile EVElectric vehicle BEVBlade electric vehicle ICVInternal combustion vehicle CICharging infrastructure Fig 1 The number of EVs BEVs CI and the pile vehicle ratio in China in 2014e2018 Z Zhao et al Journal of Cleaner Production 254 2020 1200392 based on the Bayesian pure strategic repeated game was proposed by Latifi et al 2019 Meanwhile a Stackelberg game for energy trading treating EVs as the consumers and charging stations as the energy providers was proposed by Aujla et al 2019 Previous studies have found that charging price is an important infl uence on EV users charging behaviors and EV charging costs Latinopoulos et al 2017 L Zhang et al 2019a A game model has been used to formulate interactions between the EV users and the charging operators These works have focused only on the cost benefi ts without considering the infl uence of EV consumers choice be haviors on the charging price For EV consumers charging choice behaviors the state of the charge and the attributes of the charging points i e charging price charging time location and convenience are the main motivators Yu and MacKenzie 2016 In short drivers tend to choose charging points with a reasonable charging price and short charging times which are convenient for their travel plans Yang et al 2016 A logit model to illustrate EV drivers charging choice behaviors was established by Pan et al 2019 Meanwhile a modeling framework to describe the charging behavior of BEV drivers based on cumu lative prospect theory was proposed by Hu et al 2019 Sun et al 2016 used the logit model to analyze how Japanese EV users choose charging stations and how far they are willing to detour for fast charging ones A comprehensive analysis to examine charging behavior based on the mixed logit model was performed by Zoepf et al 2013 and the attitude of EV owners toward private pile sharing and which infl uencing factors which were not as important were investigated by Plenter et al 2018 This stream of the liter ature has shown that a pricing strategy for private charging pile sharing services has not been established nor have consumers behavior choices A logit model can quantify consumers behavior choices thus providing a theoretical basis for this study A logit model is an effective way to explore consumers choice behaviors Hao et al 2018 established a binary logit model to conduct an empirical analysis of parking behavior Jianget al 2019 proposed a binary logit model to calculate the most important factor infl uencing the passengers selection Xie et al 2019 calculated customer willingness to pay WTP and identifi ed the factors affecting it using a logit model Svigelj and Hrovatin 2019 examined the important factors for choosing fi xed broadband ac cess mode in Slovenia based on a logit model Thus previous studies have illustrated that logit models are suitable for analyzing owners selection behaviors which lay the foundation for the charging sharing behavior selection modeling 1 2 Contribution The formulation of charging price is closely related to the choice of charging behavior Nevertheless the private charging pile sharing service is still immature and charging choices are uncer tain adding to the complexity of the pricing process This paper aims to explore the charging selection behaviors and price on pri vate charging pile sharing services based on a logit model and a non cooperative game model Moreover the social benefi ts of private charging pile sharing are compared to general charging behaviors to conduct a sensitivity analysis for the charging choice parameters to provide insights for policymakers This study com bines sharing theory with game theory for a new research perspective on the private charging pile sharing of EVs and pro vides a reference for charging pricing of private charging pile sharing The major contributions of this paper are as follows 1 a novel private charging pile sharing pricing model is proposed considering EV consumers charging choice behaviors 2 based on the factors infl uencing EV consumers choices the effect of these factors on the pricing of private charging pile sharing is explored extensively 3 the social and economic benefi ts of private charging pile sharing service are also explored The rest of this paper is organized as follows Section 2 analyzes EV drivers charging behavior choices and proposes a sharing rate model based on generalized costs Section 3 establishes a non cooperative game model Section 4 provides a case study of pri vate charging pile sharing benefi ts as well as managerial insights for stakeholders followed by the conclusion and suggestions for future research in Section 5 2 Sharing rate model based on the generalized cost 2 1 Factors in EV consumers charging behavior choices Generally EV consumers who do not have private piles must choose a charging pattern either public or private for energy replenishment L Zhang et al 2019a EV drivers must estimate the charging costs and services based on their analysis and choose one with minimum general cost or maximum utility benefi ts Sun et al 2016 choosing charging services to maximize their utility How ever utility is a subjective psychological feeling that is diffi cult to measure Sun et al 2016 Therefore a generalized charging cost for consumers is introduced in this study Li et al 2019 In economic management theory the generalized cost refers to the total outfl ow of all the fi nancial benefi ts caused by production and operation activities Majumdar and Mitra 2018 A consumer s charging choice behavior is a multi decision process In order to formulate the costs of private charging pile sharing and public charging services it is necessary to identify key factors infl uencing the charging choices for EV consumers Several researchers have investigated the factors determining EV consumers charging choices behavior as shown in Table 1 Current research shows that the most important factors i e charging time waiting time loca tion charging cost and accessibility etc refl ecting EV consumers charging behavior in depth In order to refi ne the calculations it was necessary to simplify the modeling scenarios Some similar characteristics i e charging status weather and SOC of EVs are not within the scope of this study Additionally the characteristic of safety should be consid ered when choosing charging patterns Plenter et al 2018 Therefore fi veelements timeliness economy convenience connection and safety are taken into consideration for modeling EV consumers charging choices as shown in Fig 2 Specifi cally there are fi ve factors the corresponding meanings will be discussed in detail later and two charging services in this logit model and the generalized cost can be calculated as Andor et al 2018 Wang and Sun 2019 Vk qs 1 Sk qt Tk qe Ek qc Ck qd Dk 1 Where Vkrepresents the generalized charging cost of the k th charging mode k b d represents private and public charging modes respectively Tkrepresentsthe utility value of the timeliness of the k th charging mode Ekrepresents the utility value of the economy of the k th charging mode Ckrepresents the utility value of the convenience of the k th charging mode Dkrepre sents the utility value of the connectivity of the k th charging mode Skrepresents the utility value of the safety of the k th charging mode andqs qt qeqc qdrepresent the weight value of the factorsofsafety timeliness economy convenience and connectivity 1 Timeliness Z Zhao et al Journal of Cleaner Production 254 2020 1200393 Timeliness represents the charging time to fi ll a standard EV with battery energy capacity W Li and Ouyang 2011 Generally total charging time consists of charging time and waiting time in the queue Y Zhang et al 2019 However with private charging pile sharing services collaborative consumption is based on a reservation scheme and the waiting time can be ignored Y Zhang et al 2019 Therefore the timeliness can be obtained as follows Tk tk VOT 2 tk W Pwk 3 VOT GNP t 4 where tkrepresents charging time W represents the battery en ergy of a standard EV Pwkrepresents the charging power of the k th charging mode VOT represents the value of time also called production time value which is calculated by the production method and generally defi ned as the value that can be created by consumers during one activity Li et al 2019 GNP represents urban per capita disposable income and t represents the average annual working hours of the driver 2 Economy Economy is the cost to fi ll a standard EV with battery energy capacity W including the charging price and parking fee Zhuge and Li 2019 Ek tk pk ek 5 where pkdenotes the charging price of the k th cha