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2019 | OriginalPaper | Buchkapitel

Heuristic Method for Optimal Deployment of Electric Vehicle Charge Stations Using Linear Programming

verfasst von : Miguel Campaña, Esteban Inga

Erschienen in: Applications of Computational Intelligence

Verlag: Springer International Publishing

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Abstract

The conventional automobile fleet has significantly increased the emission of toxic gases, thus reducing the quality of air. Therefore, this article proposes a heuristic planning model to promote the massive introduction of plug-in electric vehicles (PEV). Further, this article seeks to deploy electric vehicle charging stations (EVCS), such that the parking time to recharge a PEV are significantly reduced, according to the needs of the user. Besides, the trajectories (driving range) and vehicular flow (traffic) are considered as constraints to the planning problem, which are closely linked to the capacity of the road. On the other hand, clustering techniques are used taking into account real mobility restrictions as a function of minimum distances, and the relationship of the PEV with different charge supply subregions. At last, the model was developed in the Matlab and LpSolve environments. The former will enable the analysis of different trajectories and their relationship with its surroundings. On the other hand, the latter solves the optimization problem using the simplex method.

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Akbari, M., Brenna, M., Longo, M.: Optimal locating of electric vehicle charging stations by application of genetic algorithm. Sustainability 10(4) (2018). https://doi.org/10.3390/su10041076CrossRef

Asamer, J., Reinthaler, M., Ruthmair, M., Straub, M., Puchinger, J.: Optimizing charging station locations for urban taxi providers. Transp. Res. Part A Policy Pract. 85, 233–246 (2016). https://doi.org/10.1016/j.tra.2016.01.014CrossRef

Celli, G., Pilo, F., Monni, G., Soma, G.G.: Optimal multi-objective allocation of fast charging stations. In: 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), pp. 1–6 (2018)

Davidov, S., Pantoš, M.: Stochastic expansion planning of the electric-drive vehicle charging infrastructure. Energy 141, 189–201 (2017). https://doi.org/10.1016/j.energy.2017.09.065CrossRef

Hidalgo, P.A., Ostendorp, M., Lienkamp, M.: Optimizing the charging station placement by considering the user’s charging behavior. In: 2016 IEEE International Energy Conference, ENERGYCON 2016 (2016). https://doi.org/10.1109/ENERGYCON.2016.7513920

Inga, E., Cespedes, S., Hincapie, R., Cardenas, C.A.: Scalable route map for advanced metering infrastructure based on optimal routing of wireless heterogeneous networks. IEEE Wirel. Commun. 24(2), 26–33 (2017). https://doi.org/10.1109/MWC.2017.1600255CrossRef

Khanghah, B.Y., Anvari-Moghaddam, A., Guerrero, J.M., Vasquez, J.C.: Combined solar charging stations and energy storage units allocation for electric vehicles by considering uncertainties. In: 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), pp. 1–6, June 2017. https://doi.org/10.1109/EEEIC.2017.7977806

Lee, C., Han, J.: Benders-and-Price approach for electric vehicle charging station location problem under probabilistic travel range. Transp. Res. Part B Methodol. 106(November), 130–152 (2017). https://doi.org/10.1016/j.trb.2017.10.011CrossRef

Leeprechanon, N., Phonrattanasak, P., Sharma, M.K.: Optimal planning of public fast charging station on residential power distribution system. In: 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), pp. 519–524, June 2016. https://doi.org/10.1109/ITEC-AP.2016.7513009

10.

Ma, C., et al.: Distribution path robust optimization of electric vehicle with multiple distribution centers. PLoS One 13(3)(2018). https://doi.org/10.1371/journal.pone.0193789CrossRef

11.

Pagany, R., Marquardt, A., Zink, R.: Electric charging demand location model–a user- and destination-based locating approach for electric vehicle charging stations. Sustainability 11(8), 15 (2019). https://doi.org/10.3390/su11082301CrossRef

12.

Praveen, P., Rama, B.: An empirical comparison of Clustering using hierarchical methods and K-means. In: Proceeding of IEEE 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, IEEE - AEEICB 2016, pp. 445–449 (2016). https://doi.org/10.1109/AEEICB.2016.7538328

13.

Shi, H., Xu, M.: A data classification method using genetic algorithm and K-means algorithm with optimizing initial cluster center. In: 2018 IEEE International Conference on Computer and Communication Engineering Technology, CCET 2018, pp. 224–228 (2018). https://doi.org/10.1109/CCET.2018.8542173

14.

Shinde, P., Swarup, K.S.: A multiobjective approach for optimal allocation of charging station to electric vehicles. In: 2016 IEEE Annual India Conference (INDICON), pp. 1–6 (2016). https://doi.org/10.1109/INDICON.2016.7838934

15.

Shukla, A., Verma, K., Kumar, R.: Consumer perspective based placement of electric vehicle charging stations by clustering techniques. In: 2016 National Power Systems Conference, NPSC 2016, pp. 1–6 (2017). https://doi.org/10.1109/NPSC.2016.7858946

16.

Sun, H., Yang, J., Yang, C.: A robust optimization approach to multi-interval location-inventory and recharging planning for electric vehicles. Omega 86, 59–75 (2018). https://doi.org/10.1016/j.omega.2018.06.013CrossRef

17.

Sundström, O., Binding, C.: Planning electric-drive vehicle charging under constrained grid conditions. In: 2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON 2010 (2010), https://doi.org/10.1109/POWERCON.2010.5666620

18.

Vazifeh, M.M., Zhang, H., Santi, P., Ratti, C.: Optimizing the deployment of electric vehicle charging stations using pervasive mobility data. Transp. Res. Part A Policy Pract. 121(January), 75–91 (2019). https://doi.org/10.1016/j.tra.2019.01.002CrossRef

19.

Wang, X., Yuen, C., Hassan, N.U., An, N., Wu, W.: Electric vehicle charging station placement for urban public bus systems. IEEE Trans. Intell. Transp. Syst. 18(1), 128–139 (2017). https://doi.org/10.1109/TITS.2016.2563166CrossRef

20.

Yan, Y., Jiang, J., Zhang, W., Huang, M., Chen, Q., Wang, H.: Research on power demand suppression based on charging optimization and BESS configuration for fast-charging stations in Beijing. Appl. Sci. 8(8), 1212 (2018). https://doi.org/10.3390/app8081212. http://www.mdpi.com/2076-3417/8/8/1212CrossRef

21.

Yao-Tang, L., Lin, G., Qi, Z., Bao-Rong, Z.: Research on the modeling of EV charging station considering V2G and its optimal configuration in photovoltaic distribution network. In: 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017, pp. 0–5 (2017). https://doi.org/10.1109/ITEC-AP.2017.8081018

22.

Zhang, Q., Xu, W., Liang, W., Peng, J., Liu, T., Wang, T.: An improved algorithm for dispatching the minimum number of electric charging vehicles for wireless sensor networks. Wirel. Netw. 25(3), 1371–1384 (2019). https://doi.org/10.1007/s11276-018-1765-5CrossRef

Titel: Heuristic Method for Optimal Deployment of Electric Vehicle Charge Stations Using Linear Programming
verfasst von: Miguel Campaña
Esteban Inga
Verlag: Springer International Publishing
Buch: Applications of Computational Intelligence
Print ISBN: 978-3-030-36210-2

Electronic ISBN: 978-3-030-36211-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-36211-9_20

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