Abstract
Smart grid is an emerging complex system of systems that continually provide distributed communication service to enable an efficient, optimal, reliable, secure energy transmission and distribution. Rapid growth in the number of Electric Vehicles (EVs) in use would have a significant impact on the capacity and efficiency of electric power network and thus post great challenges on the smart grid technology. The extent to which the scheduling can benefit the system depends greatly on the dynamic EV mobility pattern and levels of electricity usage of EVs. EV mobility simulation is useful in analyzing how charging scheduling works. In this paper, we focus on the need for human mobility model based simulation in smart grid to help validate the performance of charging scheduling of EVs in a distributed smart grid system. The mobility model simulates the driver’s driving actions in micro domain such as steer, speed up and brake etc. to synthesize smooth, reasonable and realistic vehicle trajectory. The simulated EVs mobility can be configured to produce real world dynamic mobility pattern of a large scale of individual EVs in a certain fine grain. The charge scheduling algorithms are introduced in the simulation to validate the feasibility of the EV mobility model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boots, M., Thielens, D., Verheij, F.: International example developments in Smart Grids - Possibilities for application in the Netherlands, confidential report for the Dutch Government, KEMA Nederland B.V., Arnhem (2010)
Cacciapuoti, A.S., Calabrese, F., Caleffi, M., et al.: Human-mobility enabled networks in urban environments: is there any (mobile wireless) small world out there? Ad Hoc Netw. 10, 1520–1531 (2012)
Gonzalez, M., Hidalgo, C., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008). doi:10.1038/nature06958
Nor, J.K.: Art of charging electric vehicle batteries. In: WESCON 1993 Conference, California, USA, pp. 521–525 (1993)
(2008). http://www.matsim.org
Tang, J., Musolesi, M., Mascolo, C., Latora, V.: Temporal distance metrics for social network analysis. In: WOSN 2009, Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 31–36 (2009)
Leguay, J., Lindgren, A., Scott, J., Friedman, T., Crowcroft, J.: Opportunistic content distribution in an urban setting. In: CHANTS 2006: Proceedings of the 2006 SIGCOMM Workshop on Challenged Networks, pp. 205–212 (2006)
Hui, P., Crowcroft, J., Yoneki, E.: Bubble rap: social-based forwarding in delay tolerant networks. In: MobiHoc 2008: Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 241–250 (2008)
Acknowledgement
This research was supported by “12nd Five-year Plan” for Sci & Tech Research of China (No. 2012BAH38X).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, T., Liu, L., Song, S., Yuan, Y. (2015). Human Mobility Simulation in Smart Energy Grid. In: Yueming, L., Xu, W., Xi, Z. (eds) Trustworthy Computing and Services. ISCTCS 2014. Communications in Computer and Information Science, vol 520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47401-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-662-47401-3_30
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47400-6
Online ISBN: 978-3-662-47401-3
eBook Packages: Computer ScienceComputer Science (R0)