Abstract
Electric vehicles are one of the strongest ways for society to stop contributing to greenhouse gas emissions. However, for their use to become regular, a good infrastructure of charging stations is needed, allowing a similar convenience to that offered by fossil fuel stations. Our work approaches the location of charging stations to create a nationwide infrastructure. In this case, we focus on Spain and using genetic algorithms, we search for and evaluate different configurations according to the number of stations desired. Our results show that, with 250 stations, an initial infrastructure that covers most of the territory can be developed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
For a set of points, the Delaunay triangulation satisfies that none of those points will be inside the circumference of any of the triangles. This triangulation is closely related to the Voronoi diagram since the circumferences of the Delaunay triangles are the vertices of the Voronoi diagram (i.e., of the polygons that form it).
- 8.
- 9.
References
Colmenar-Santos, A., De Palacio, C., Borge-Diez, D., Monzón-Alejandro, O.: Planning minimum interurban fast charging infrastructure for electric vehicles: methodology and application to Spain. Energies 7(3), 1207–1229 (2014)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). https://doi.org/10.1109/4235.996017
Dong, J., Liu, C., Lin, Z.: Charging infrastructure planning for promoting battery electric vehicles: an activity-based approach using multiday travel data. Transp. Res. Part C: Emerg. Technol. 38, 44–55 (2014)
Jordán, J., Palanca, J., Del Val, E., Julian, V., Botti, V.: A multi-agent system for the dynamic emplacement of electric vehicle charging stations. Appl. Sci. 8(2), 313 (2018). https://doi.org/10.3390/app8020313
Knapen, L., Kochan, B., Bellemans, T., Janssens, D., Wets, G.: Activity based models for countrywide electric vehicle power demand calculation. In: 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS), pp. 13–18. IEEE (2011)
Neubauer, J., Wood, E.: The impact of range anxiety and home, workplace, and public charging infrastructure on simulated battery electric vehicle lifetime utility. J. Power Sour. 257, 12–20 (2014)
Palanca, J., Jordán, J., Bajo, J., Botti, V.: An energy-aware algorithm for electric vehicle infrastructures in smart cities. Futur. Gener. Comput. Syst. 108, 454–466 (2020)
Pevec, D., Babic, J., Carvalho, A., Ghiassi-Farrokhfal, Y., Ketter, W., Podobnik, V.: A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety. J. Clean. Prod. 276, 122779 (2020)
Tu, W., Li, Q., Fang, Z., Lung Shaw, S., Zhou, B., Chang, X.: Optimizing the locations of electric taxi charging stations: a spatial-temporal demand coverage approach. Transp. Res. Part C: Emerg. Technol. 65, 172–189 (2016). https://doi.org/10.1016/j.trc.2015.10.004
Victor-Gallardo, L., et al.: Strategic location of EV fast charging stations: the real case of Costa Rica. In: 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), pp. 1–6. IEEE (2019)
Acknowledgments
This work was partially supported by the MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Pasqual Martí is funded by grant PAID-01-20-4 of Universitat Politècnica de València.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Jordán, J., Martí, P., Palanca, J., Julian, V., Botti, V. (2021). Interurban Electric Vehicle Charging Stations Through Genetic Algorithms. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-86271-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86270-1
Online ISBN: 978-3-030-86271-8
eBook Packages: Computer ScienceComputer Science (R0)