Spatial and Temporal Charging Infrastructure Planning Using Discrete Event Simulation

ABSTRACT

The switch from gasoline-powered vehicles to electric vehicles (EVs) is an important step to reduce greenhouse gas emissions. To this end, many European countries announced EV stock targets, e.g., Germany aims to have one million EVs on the roads by 2020. To achieve these goals and to handle the range limitation of EVs, a widespread publicly accessible charging infrastructure is needed. This paper provides a dynamic spatial and temporal simulation model for the building of charging infrastructure on a municipality scale. We evaluate empirical data about the timely utilization of different charging stations in the German federal state of Bavaria. This data is used to derive empirical models for the start time and duration of charging events as well as the popularity of charging stations. We develop a lightweight discrete event simulation model which can be used to investigate different expansion strategies, e.g., based on the load of charging stations or the number of successful and failed charging attempts. We show the applicability of our model using the German federal state of Bavaria as a use case.

References

1. I. E. Agency. Global EV Outlook 2016 - Beyond one million electric cars, 2016. https://www.iea.org/publications/freepublications/publication/Global_EV_Outlook_2016.pdf.
2. Bavarian State Government. Gemeinsame Position der Bayerischen Staatsregierung und der bayerischen Automobilhersteller zur Elektromobilitat, 2015. https://www.stmwi.bayern.de/fileadmin/user_upload/stmwivt/Themen/Initiativen/Dokumente/2016-01--26-Gemeinsame_Position_der_Bayerischen_Staatsregierung.pdf.
3. W. Brost, T. Funke, and D. Vallee. SLAM - Schnellladenetz für Achsen und Metropolen. In DVWG Jahresverkehrskongress 2016: Elektromobilitat - aktuelle Chancen und Risiken der Umsetzung, pages 1--3, 2016.
4. C. H. Dharmakeerthi, N. Mithulananthan, and T. K. Saha. Planning of electric vehicle charging infrastructure. In 2013 IEEE Power Energy Society General Meeting, pages 1--5, July 2013.
5. A. Y. S. Lam, Y. W. Leung, and X. Chu. Electric vehicle charging station placement: Formulation, complexity, and solutions. IEEE Transactions on Smart Grid, 5(6):2846--2856, Nov 2014.
6. G. Mauri and A. Valsecchi. Fast charging stations for electric vehicle: The impact on the mv distribution grids of the milan metropolitan area. In 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), pages 1055--1059, Sept 2012.
7. Nationale Plattform Elektromobilitat. Charging Infrastrucutre for Electric Vehicles in Germany - Progress Report and Recommendations 2015, 2015. http://nationale-plattform-elektromobilitaet.de/fileadmin/user_upload/Redaktion/AG3_Statusbericht_LIS_2015_engl_klein_bf.pdf.
8. N. Pearre, W. Kempton, R. Guensler, and V. Elango. Electric vehicles: How much range is required for a days driving? Transportation Research Part C: Emerging Technologies, 19(6):1171--1184, December 2011.
9. R. Bi and J. Xiao and V. Viswanathan and A. Knoll. Influence of Charging Behaviour Given Charging Station Placement at Existing Petrol Stations and Residential Car Park Locations in Singapore. Procedia Computer Science, 80:335 -- 344, 2016.
10. V. Viswanathan, D. Zehe, J. Ivanchev, D. Pelzer, A. Knoll, and H. Aydt. Simulation-assisted exploration of charging infrastructure requirements for electric vehicles in urban environments. Journal of Computational Science, 12:1--10, 2016.
11. J. Wirges. Planning the Charging Infrastructure for Electric Vehicles in Cities and Regions. KIT Scientific Publishing, Karlsruhe, Germany, 2016.

Index Terms

• Computing methodologies

  • Modeling and simulation

    • Simulation types and techniques

      • Discrete-event simulation