Abstract
Large scale of electric vehicles (EVS) integration will pose great impacts on the power system, due to their disorderly charging. Electric cars’ charging load cannot be forecasted as the traditional power load, which is usually forecasted based on historical data. There need to be some other methods to predict electric vehicles charging load, in order to improve the reliability and security of the grid. This paper analyze the travel characteristics of electric vehicles, then use the fuzzy inference system to emulate the process of drivers’ decision to charge their cars, the charging probability is attained in the given location. Finally, the daily profile of charging load can be predicted according to the numbers of electric vehicles forecasted in Beijing.
Fund project: the national natural science foundation of China(51277057); Research of load forecasting theory and method under smart grid environment.
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
Preview
Unable to display preview. Download preview PDF.
References
Zhang, W., Wu, B., Li, W., Lai, X.: Discussion on Development Trend of Battery Electric Vehicles in China and Its Energy Supply Mode. Power System Technology 33(4), 1–5 (2009)
Xin, H.: The development of low carbon economy and electric vehicles: trends and counter measures. China Opening Herald (5), 31–35 (2009)
Hu, Z., Song, Y., Xu, Z.: Impacts and Utilization of Electric Vehicles Integration Into Power Systems. Proceedings of the CSEE 32(4), 1–10 (2012)
Tian, L., Shi, S., Jia, Z.: A Statistical Model for Charging Power Demand of Electric Vehicles. Power System Technology 11, 126–130 (2010)
Tikka, V., Lassila, J., Haakana, J., Partanen, J. (eds.): Case study of the effects of electric vehicle charging on grid loads in an urban area. In: 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe). IEEE (2011)
Tang, D., Wang, P. (eds.): Dynamic electric vehicle charging load modeling: From perspective of transportation. In: 4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). IEEE (2013)
Adornato, B., Patil, R., Filipi, Z., Bareket, Z., Gordon, T.: Characterizing naturalistic driving patterns for Plug-in Hybrid Electric Vehicle analysis. In: IEEE VPPC 2009, Ann Arbor, MI, pp. 655–660 (2009)
ECO tality, “The EV project,” Q1-2012 Report
Pan, C., Zhao, S.: The analysis of different parking behavior with different trip purpose. Construction Economy 1 (2011)
Luo, Z., Hu, Z., Song, Y.: Calculation method of EV charging load. Automation of Electric Power System 14, 36–42 (2011)
Lee, T.K., Adornato, B., Filipi, Z.S.: Synthesis of real-world driving cycles and their use for estimating PHEV energy consumption and charging opportunities: Case study for Midwest/US. IEEE Transactions on Vehicular Technology 60(9), 4153–4163 (2011)
Beijing Traffic Development Report in 2005
The third travel survey of Beijing residents
Li, Z., Zong, F., Zhang, B.: Study on Parking Information Behavior in Large and Medium-sized Cities. Information Science 29(12), 1896–1901 (2012)
Wang, Q.: The Research on Fuzzy Inference of Online Consumers’ Buying Intention. Dalian University of Technology (2013)
Liu, H.: Mamdani fuzzy reasoning algorithm and its Matlab realization. The Science Education Article Collects 10, 269–271 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jingwei, Y., Diansheng, L., Shuang, Y., Shiyu, H. (2014). Charging Load Forecasting for Electric Vehicles Based on Fuzzy Inference. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_62
Download citation
DOI: https://doi.org/10.1007/978-3-662-45643-9_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45642-2
Online ISBN: 978-3-662-45643-9
eBook Packages: Computer ScienceComputer Science (R0)