A Stochastic Programming Approach for Electric Vehicle Charging Network Design

The advantages of electric vehicles (EVs) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community, considering uncertainties in the arrival and dwell times of vehicles, the state of charge of arriving vehicles’ batteries, drivers’ walking ranges and charging preferences, demand during weekdays and weekends, and the community’s rate of EV adoption. We conducted studies using the sample average approximation method, which asymptotically converges to an optimal solution for a two-stage stochastic problem. However, this method is computationally expensive for large-scale instances. Therefore, we also developed a heuristic to produce nearly optimal solutions quickly for our data instances. We conducted computational experiments using various publicly available data sources and evaluated the benefits of the solutions for a given community, both quantitatively and qualitatively.

[1]  Zhiwei Xu,et al.  Optimal Coordination of Plug-In Electric Vehicles in Power Grids With Cost-Benefit Analysis—Part I: Enabling Techniques , 2013, IEEE Transactions on Power Systems.

[2]  Margaret Smith,et al.  Costs Associated With Non-Residential Electric Vehicle Supply Equipment: Factors to consider in the implementation of electric vehicle charging stations , 2015 .

[3]  Man Zhang,et al.  Optimization of electric vehicle charging capacity in a parking lot for reducing peak and filling valley in power grid , 2011, 2011 International Conference on Advanced Power System Automation and Protection.

[4]  Daniel Berger,et al.  A Comparison of Carbon Dioxide Emissions from Electric Vehicles to Emissions from Internal Combustion Vehicles , 2015 .

[5]  Russell Bent,et al.  Locating PHEV Exchange Stations in V2G , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[6]  A. Diez-Roux,et al.  Walking distance by trip purpose and population subgroups. , 2012, American journal of preventive medicine.

[7]  V. Jorge Leon,et al.  An arc cover-path-cover formulation and strategic analysis of alternative-fuel station locations , 2013, Eur. J. Oper. Res..

[8]  Yan Zhou,et al.  An optimization framework for workplace charging strategies , 2015 .

[9]  M. Kuby,et al.  A Model for Location of Capacitated Alternative-Fuel Stations , 2009 .

[10]  S. A. MirHassani,et al.  Refueling-station location problem under uncertainty , 2015 .

[11]  Wesley E. Marshall,et al.  An evaluation of livability in creating transit-enriched communities for improved regional benefits , 2013 .

[12]  Wei-Hua Lin,et al.  A stochastic flow capturing location and allocation model for siting electric vehicle charging stations , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[13]  Fei Wu,et al.  A stochastic flow-capturing model to optimize the location of fast-charging stations with uncertain electric vehicle flows , 2017 .

[14]  Xuewen Lu,et al.  Studying Differences of Household Weekday and Weekend Activities , 2008 .

[15]  Aydogan Ozdemir,et al.  Distributed storage capacity modelling of EV parking lots , 2015, 2015 9th International Conference on Electrical and Electronics Engineering (ELECO).

[16]  Zhipeng Liu,et al.  Optimal Planning of Electric-Vehicle Charging Stations in Distribution Systems , 2013, IEEE Transactions on Power Delivery.

[17]  Fouad Baouche,et al.  Efficient Allocation of Electric Vehicles Charging Stations: Optimization Model and Application to a Dense Urban Network , 2014, IEEE Intelligent Transportation Systems Magazine.

[18]  Xiaowen Chu,et al.  Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions , 2013, IEEE Transactions on Smart Grid.

[19]  Zhiwei Xu,et al.  Optimal Coordination of Plug-in Electric Vehicles in Power Grids With Cost-Benefit Analysis—Part II: A Case Study in China , 2013, IEEE Transactions on Power Systems.

[20]  John Krumm,et al.  How People Use Their Vehicles: Statistics from the 2009 National Household Travel Survey , 2012 .

[21]  Michael Kuby,et al.  An efficient formulation of the flow refueling location model for alternative-fuel stations , 2012 .

[22]  Todd Litman,et al.  Economic Value of Walkability Economic Value of Walkability , 2022 .

[23]  David P. Morton,et al.  Monte Carlo bounding techniques for determining solution quality in stochastic programs , 1999, Oper. Res. Lett..

[24]  B. Wee,et al.  The influence of financial incentives and other socio-economic factors on electric vehicle adoption , 2014 .

[25]  Ying-Wei Wang,et al.  Locating multiple types of recharging stations for battery-powered electric vehicle transport , 2013 .

[26]  Sydney Vergis,et al.  Comparison of plug-in electric vehicle adoption in the United States: A state by state approach , 2015 .

[27]  T. Tsiligirides,et al.  Heuristic Methods Applied to Orienteering , 1984 .

[28]  R. Paul Brooker,et al.  Identification of potential locations of electric vehicle supply equipment , 2015 .

[29]  Yafeng Yin,et al.  Deploying public charging stations for electric vehicles on urban road networks , 2015 .

[30]  Hua Cai,et al.  Optimal locations of electric public charging stations using real world vehicle travel patterns , 2015 .

[31]  Rachel M. Krause,et al.  Intent to Purchase a Plug-In Electric Vehicle: A Survey of Early Impressions in Large US Cites , 2013 .

[32]  Gonçalo Homem de Almeida Correia,et al.  A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours , 2015 .

[33]  John R. Birge,et al.  The value of the stochastic solution in stochastic linear programs with fixed recourse , 1982, Math. Program..