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Optimal Building Policies of Charging Stations with Automobile Supply Chain Analysis

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  • 1. School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China;
    2. School of Management, Huazhong University of Science and Technology, Wuhan 430074, China

Received date: 2016-12-19

  Revised date: 2017-12-26

  Online published: 2018-12-25

Abstract

Greenhouse gases and criteria pollutants from transportation vehicles harm our green environment. Electric vehicles (EVs) offer great potential to dramatically reduce local air pollution and greenhouse gas emissions. Switching the conventional transportation vehicles (petroleum) to alternative fuel vehicles (electricity) is the goal of the governments in China, the U.S. and Europe. However, range anxiety, which is caused by the very limited driving range of EVs, impedes potential consumers from adopting EVs. The question of alleviating range anxiety has become increasingly important for achieving a large adoption level of EVs.
As the associated charging infrastructure building plays a key role in affecting the psychological understanding of consumers. The extension of charging network is an approach to alleviate consumers' range anxiety and to increase the EV adoption level. The automobile supply chain members, EV manufacturers and EV dealers, have incentives to offer building investment on charging network because that the expansion of charging ability on the road increases the willingness to pay of potential consumers, which may enable them to sell more cars and set higher prices. Two business models, M-Building (manufacturers take the charge of the building) and D-Building (dealers take the charge of the building) are considered and finding which one, the EV manufacturer of the EV dealer, is more suitable to take the charge of building charging network for larger EV adoption level is focused on. In addition, the preferences of the manufacturer and the dealer on the choices between M-Building and D-Building are also provided.
Specifically, the Stackelberg Game is used to model the question in two scenarios, wholesale price is fixed or a decision made by the manufacturer. Optimal pricing and building strategies are obtained through sequential backward induction. Used values in the numerical studies are normalized by satisfying our model specifics. Our conclusions show that when the wholesale price is fixed, M-Building facilitates the adopting of EVs better at the beginning stage of the development of an EV market; D-Building facilitates the adoption of EVs better when the market becomes mature. However, neither of the two builders have an incentive to offer building investment. When the wholesale price is a decision made by the manufacturer, M-Building always better facilitates EV adoption. The manufacturer is voluntary even with high building costs.
Our paper can provide the governors and builders with rich insights and recommendations. First, governors should subsidize the builders especially when the market is at its early stage. Second, the participation of EV dealers in building charging network is very important for the mass adoption. Finally, governors should pave the ways for implementing D-Building.

Cite this article

WANG Tian, DENG Shi-ming . Optimal Building Policies of Charging Stations with Automobile Supply Chain Analysis[J]. Chinese Journal of Management Science, 2018 , 26(10) : 152 -163 . DOI: 10.16381/j.cnki.issn1003-207x.2018.10.015

References

[1] Bradley M J. Electric vehicle grid integration in the U. S., Europe, and China[R]. 2013.

[2] Plumer B. What's wrong with the electric car? Psychology, perhaps[N]. Washington Post, 2011.

[3] Garthwaite J. Range anxiety:Fact or fiction?[N]. National Geographic News, 2011.

[4] Edelstein S.Ford to install electric-car charging stations at company sites[R]. Green Car Report. 2013.

[5] Hall D, Lutsey N. Emerging best practices for electric vehicle charging infrastructure[J]. Washington, DC:The International Council on Clean Transportation (ICCT), 2017.

[6] Luo Chao, Huang Y F, Gupta V. Stochastic dynamic pricing for EV charging stations with renewables integration and energy storage[J]. IEEE Transactions on Smart Grid, 2018,9(2):1494-1505.

[7] Kuby M, Lim S. The flow-refueling location problem for alternative-fuel vehicles[J]. Socio-Economic Planning Sciences, 2005, 39(2):125-145.

[8] Kuby M, Lim S. Location of alternative-fuel stations using the flow-refueling location model and dispersion of candidate sites on arcs[J]. Networks and Spatial Economics, 2007, 7(2):129-152.

[9] Upchurch C, Kuby M, Lim S. A model for location of capacitated alternative-fuel stations[J]. Geographical Analysis, 2009, 41(1):85-106.

[10] 赵容, 刘克艳, 任佩瑜. 路段通行能力不同的避难点选址模型及算法[J]. 中国管理科学, 2017,25(9):133-140.

[11] Mak H Y, Rong Ying, Shen Z J M. Infrastructure planning for electric vehicles with battery swapping[J]. Management Science, 2013, 59(7):1557-1575.

[12] 王蕾, 陈希. 中国农村地区可持续医疗中心的选址与优化方法[J]. 中国管理科学, 2016,24(s1):38-42.

[13] 任玉珑, 史乐峰, 张谦,等. 电动汽车充电站最优分布和规模研究[J]. 电力系统自动化, 2011, 35(14):53-57.

[14] 孙小慧, 刘锴, 左志. 考虑时空间限制的电动汽车充电站布局模型[J]. 地理科学进展, 2012, 31(6):686-692.

[15] 王辉, 王贵斌, 赵俊华,等. 考虑交通网络流量的电动汽车充电站规划[J]. 电力系统自动化, 2013, 37(13):63-69.

[16] 杨珺, 冯鹏祥, 孙昊,等. 电动汽车物流配送系统的换电站选址与路径优化问题研究[J]. 中国管理科学, 2015, 23(9):87-96.

[17] 滕耘, 胡天军, 卫振林. 电动汽车充电电价定价分析[J]. 交通运输系统工程与信息, 2008, 8(3):126-130.

[18] Chauvet F, Hafez N, Proth J M. Electric vehicles:Effect of the availability threshold on the transportation cost[J]. Applied Stochastic Models in Business and Industry, 1999, 15(3):169-181.

[19] Kristoffersen T K, Capion K, Meibom P. Optimal charging of electric drive vehicles in a market environment[J]. Applied Energy, 2011, 88(5):1940-1948.

[20] 黄守军, 杨俊, 陈其安. 基于B-S期权定价模型的V2G备用合约协调机制研究[J]. 北京:中国管理科学, 2016, 24(10):10-21.

[21] 王敏楠. 电动汽车及其充电网络价值评估[D]. 北京:华北电力大学, 2014.

[22] 夏露, 刘畅, 李斌,等. 面向私人电动汽车的城市公共充电网络运营服务能力评估方法与仿真研究[J]. 电网技术, 2015, 39(12):3543-3548.

[23] He Fang, Yin Yafeng, Zhou Jing. Deploying public charging stations for electric vehicles on urban road networks[J]. Transportation Research Part C:Emerging Technologies, 2015, 60:227-240.

[24] Lim M K, Mak H Y, Rong Ying. Toward mass adoption of electric vehicles:Impact of the range and resale anxieties[J]. Manufacturing & Service Operations Management, 2015, 17(1):101-119.

[25] 周健, 华中生, 尹建伟,等. 基于平均电价的在线电动汽车充电排程定价机制[J]. 管理科学学报, 2018,21(1):1-12.

[26] Avci B, Girotra K, Netessine S. Electric vehicles with a battery switching station:adoption and environmental impact[J]. Management Science, 2014, 61(4):772-794.

[27] Nourbakhsh S M, Ouyang Yangfeng. Optimal fueling strategies for locomotive fleets in railroad networks[J]. Transportation Research Part B:Methodological, 2010, 44(8-9):1104-1114.

[28] Kang S, Önal H, Ouyang Yangfeng, et al. Optimizing the biofuels infrastructure:transportation networks and biorefinery locations in Illinois[G]. Handbook of Bioenergy Economics and Policy. New York, NY:Springer New York, 2010:151-173.

[29] Bai Yun, Hwang T, Kang S, et al. Biofuel refinery location and supply chain planning under traffic congestion[J]. Transportation Research Part B:Methodological, 2011, 45(1):162-175.

[30] Huang Jian, Leng Mingming, Liang Liping, et al. Promoting electric automobiles:supply chain analysis under a government's subsidy incentive scheme[J]. ⅡE Transactions, 2013, 45(8):826-844.

[31] Luo Chunlin, Leng Mingming, Huang Jian, et al. Supply chain analysis under a price-discount incentive scheme for electric vehicles[J]. European Journal of Operational Research, 2014, 235(1):329-333.

[32] 钟太勇, 杜荣. 基于博弈论的新能源汽车补贴策略研究[J]. 中国管理科学, 2015,23(s1):817-822.

[33] 杨艳萍,闫宏斌,马铁驹. 基于模糊认知图的纯电动汽车扩散分析[J]. 系统管理学报, 2018,27(2):359-365.

[34] 蒋然, 李英. 基于TOPSIS的消费者新能源汽车购买决策模型及仿真[J]. 中国管理科学, 2014,22(s1):718-723.

[35] 黄毅祥, 蒲勇健. 新能源汽车分时租赁市场竞争的进化博弈模型研究[J]. 中国管理科学, 2018,26(2):79-85.

[36] 经有国, 郭培强, 秦开大. 需求率受推广努力水平影响的新能源汽车租赁系统协调契约[J]. 中国管理科学, 2018,26(3):94-100.

[37] Hotelling H. Stability in competition[J]. Economic Journal, 1929, 39:41-57.

[38] Tabuchi T. Urban agglomeration economies in a linear city[J]. Regional Science & Urban Economics, 1986, 16(3):421-436.

[39] Zhang Xiaoning, Huang Haijun, Zhang H M. Integrated daily commuting patterns and optimal road tolls and parking fees in a linear city[J]. Transportation Research Part B:Methodological, 2008, 42(1):38-56.
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