Acta Univ. Agric. Silvic. Mendelianae Brun. 2017, 65(5), 1741-1750 | DOI: 10.11118/actaun201765051741
A Model of Charging Service Demand for the Czech Republic
- Department of Informatics, Faculty of Business and Management, Brno University of Technology, Kolejní 2906/4, 612 00 Brno, Czech Republic
The paper introduces a standalone model of electric vehicle charging demand based on large-scale travel survey data of the Czech Republic. This demand model has been intended as a comprehensive input model for following charging infrastructure problem, where a spatial view of charging demand is usually needed. The model uses publicly available data, whose mutual incompatibility and information richness had to be overcome. The necessary data transformations are described and final data representation in the form of a mathematical graph allows the introduction of a point-defined (vertex-defined) charging demand model. Several drawbacks of the model are identified and their effect, as well as an application of whole model, is demonstrated on the large-scale numerical example. Sound demand model is a cornerstone for demand-related problems, such as general large-scale charging infrastructure problem, which is a common issue for countries that stand at the very beginning of the electric vehicle adoption process.
Keywords: electric vehicles, charging service demand, charging stations, graph theory, Czech Republic road network, charging demand model, charging service, charging infrastructure, traffic survey, traffic data
Grants and funding:
This research was supported by project no. FP-J-17-4174 - Use of Artificial Intelligence in Entrepreneurship - at the Department of Informatics at the Faculty of Business and Management of Brno University of Technology.
Published: October 31, 2017 Show citation
References
- AMINI, M. H., KARGARIAN, A. and KARABASOGLU, O. 2016. ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electric Power Systems Research, 140: 378 - 390. DOI: 10.1016/j.epsr.2016.06.003
Go to original source...
- BAE, S. and KWASINSKI, A. 2012. Spatial and Temporal Model of Electric Vehicle Charging Demand, IEEE Transactions on Smart Grid, 3(1): 394 - 403. DOI: 10.1109/TSG.2011.2159278
Go to original source...
- CAVADAS, J., DE ALMEIDA CORREIA, G. H. and GOUVEIA, J. 2015. A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours. Transportation Research Part E: Logistics and Transportation Review, 75: 188 - 201. DOI: 10.1016/j.tre.2014.11.005
Go to original source...
- CZECH REPUBLIC. 1997. Zákon 13/1997 Sb. zed ne 23. ledna 1997 o pozemních komunikacích. In: Sbírka zákonů České republiky. Částka 3/1997. Available at: https://portal.gov.cz/app/zakony/zakonPar.jsp?idBiblio=44836&fulltext=13~2F1997&rpp=15#local-content [Accessed: 24. 8. 2016].
- FAN, Z. 2012. A Distributed Demand Response Algorithm and Its Application to PHEV Charging in Smart Grids. IEEE Transactions on Smart Grid, 3(3): 1280 - 1290. DOI: 10.1109/TSG.2012.2185075
Go to original source...
- GE, S., FENG, L. and LIU, H. 2011. The planning of electric vehicle charging station based on Grid partition method. In: 2011 International Conference on Electrical and Control Engineering. 16 - 18 Sept. 2011, Yichang, China.
Go to original source...
- GONZÁLEZ, J., ALVARO, R., GAMALLO, C., FUENTES, M., FRAILE-ARDANUY, J., KNAPEN, L. and JANSSENS, D. 2014. Determining Electric Vehicle Charging Point Locations Considering Drivers' Daily Activities. Procedia Computer Science, 32: 647 - 654. DOI: 10.1016/j.procs.2014.05.472
Go to original source...
- JAMIAN, J. J., MUSTAFA, M. W., MOKHLIS, H. and BAHARUDIN, M. A. 2014. Simulation study on optimal placement and sizing of Battery Switching Station units using Artificial Bee Colony algorithm. International Journal of Electrical Power & Energy Systems, 55: 592 - 601. DOI: 10.1016/j.ijepes.2013.10.009
Go to original source...
- LAM, A. Y. S., LEUNG, Y. and CHU, Y. 2014. Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions. IEEE Transactions on Smart Grid, 5(6): 2846 - 2856. DOI: 10.1109/TSG.2014.2344684
Go to original source...
- LI, G. and ZHANG, X. 2012. Modelling of Plug-in Hybrid Electric Vehicle Charging Demand in Probabilistic Power Flow Calculations. IEEE Transactions on Smart Grid, 3(1): 492 - 499. DOI: 10.1109/TSG.2011.2172643
Go to original source...
- LIM, S. and KUBY, M. 2010. Heuristic algorithms for siting alternative-fuel stations using the Flow-Refueling Location Model. European Journal of Operational Research, 204(1): 51 - 61. DOI: 10.1016/j.ejor.2009.09.032
Go to original source...
- MADINA, C., ZAMORA, I. and ZABALA, E. 2016. Methodology for assessing electric vehicle charging infrastructure business models. Energy Policy, 89: 284 - 293. DOI: 10.1016/j.enpol.2015.12.007
Go to original source...
- MAHESHWARI, P., TAMBAWALA, Y., NUNNA, H. S. V. S. K. and DOOLLA, S. 2014. A Review on Plug-in Electric Vehicles Charging: Standards and Impact on Distribution System. In: IEEE International Conference on Power Electronics Drives and Energy Systems 2014, 16 - 19 Dec. 2014 Mumbai, India.
Go to original source...
- PEKÁREK, J. 2015a. A Dynamical Model of the Charging Station Placement Problem. In: Innovation Vision 2020: From Regional Development Sustainability to Global Economic Growth 25. Amsterdam, Netherlands: International Business Information Management Association (IBIMA), pp. 2292 - 2302.
- PEKÁREK, J. 2015b. Determination of Electric Vehicle Charging Demand. In: Innovation Management and Sustainable Economic Competitive Advantage: From Regional Development to Global Growth 26. Madrid, Spain: International Business Information Management Association (IBIMA), pp. 1211 - 1220.
- ROAD AND MOTORWAY DIRECTORATE. 2016a. Vector data of Czech Republic road network. Road Database and NDIC Department, Road and Motorway Directorate, Praha, Czech Republic. [Obtained: 10. 12. 2014].
- ROAD AND MOTORWAY DIRECTORATE. 2016b. National Traffic Survey 2010. Road and Motorway Directorate in cooperation with the Transport Research Centre, Prague, Czech Republic [Obtained: 10. 7. 2015].
- SEARS, J., GLITMAN, K. and ROBERTS, D. 2014. Forecasting demand of public electric vehicle charging infrastructure. In: 2014 IEEE Conference on Technologies for Sustainability (SusTech), Portland, USA, 2014-07-24, pp. 250 - 254.
Go to original source...
- TU, W., LI, Q., FANG, Z., SHAW, S. and ZHOU, B. 2016. Optimizing the locations of electric taxi charging stations: A spatial-temporal demand coverage approach. Transportation Research Part C: Emerging Technologies, 65: 172 - 189. DOI: 10.1016/j.trc.2015.10.004
Go to original source...
- U.S. DEPARTMENT OF TRANSPORTATION, FEDERAL HIGHWAY ADMINISTRATION. 2009. National Household Travel Survey [Online]. Available at: http://nhts.ornl.gov [Accessed: 17. 9. 2016].
- WU, B. Y. and CHAO, K. M. 2004. Spanning trees and optimization problems. Boca Raton, FL: Chapman & Hall/CRC.
Go to original source...
- YI, Z. and BAUER, P. H. 2016. Optimization models for placement of an energy-aware electric vehicle charging infrastructure, Transportation Research Part E: Logistics and Transportation Review, 91: 227 - 244. DOI: 10.1016/j.tre.2016.04.013
Go to original source...
- ZHU, Z., GAO, Z., ZHENG, J. and DU, M. 2016. Charging station location problem of plug-in electric vehicles. Journal of Transport Geography, 52: 11 - 22. DOI: 10.1016/j.jtrangeo.2016.02.002
Go to original source...
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