NDLI: Charging demand for electric vehicle based on stochastic analysis of trip chain

Content Provider	IET Digital Library
Author	Shun, Tao Kunyu, Liao Xiangning, Xiao Jianfeng, Wen Yang, Yang Jian, Zhang
Abstract	With its popularisation, electric vehicle (EV) has been part of the important load of smart grid. The analysis on charging demands of EVs is the basis of the study of influences on power grid and charging infrastructures planning. Charging demand calculation is based on the precise analysis on the driving law of users. This study proposes a method based on the stochastic simulation of trip chain to solve the existing problem. First, the concept and characteristic variables (trip start time, driving time, parking duration, driving distance and trip purpose) of trip chain are introduced. Second, the probability distribution models of these variables are established considering their correlative relationships. Third, using Monte Carlo method, the complete trip chains are simulated, and then the spatial–temporal distributions of charging demands are calculated. Finally, based on the National household trip survey data, and according to the proposed method, charging demands in different areas are studied. The results show that the proposed method can accurately simulate the driving law of users and reflect spatial–temporal distribution characteristics of charging demands, and different charging scenarios will lead to different forms of charging demand distribution, which will exert different impacts on power system.
Starting Page	2689
Ending Page	2698
Page Count	10
ISSN	17518687
Volume Number	10
e-ISSN	17518695
Issue Number	Issue 11, Aug (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/10/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2015.0995
Journal	IET Generation, Transmission & Distribution
Publisher Date	2016-08-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Characteristic Variables Charging Demand Calculation Charging Infrastructures Planning Concept Variables Driving Distance Driving Law Driving Time Electric Vehicle Monte Carlo Method National Household Trip Survey Parking Duration Popularisation Power System Probability Distribution Model Smart Grid Smart Power Grid Spatial-temporal Distribution Stochastic Analysis Stochastic Linearised SCUC Transportation Trip Chain Trip Purpose Trip Start Time
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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