An Internet-Inspired Proportional Fair EV Charging Control Method

Transportation systems are undergoing a major transition with the integration of electric vehicles (EVs). However, due to increase in battery energy and charger power ratings, potential adverse effects on the distribution grid is a crucial issue to be addressed. Large voltage drops at charging nodes...

Full description

Saved in:
Bibliographic Details
Published in:IEEE systems journal 2019-12, Vol.13 (4), p.4292-4302
Main Authors: Ucer, Emin, Kisacikoglu, Mithat C., Yuksel, Murat, Gurbuz, Ali Cafer
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
Battery chargers
Charging stations
complex networks
computer networks
Congestion
distributed managament
Electric potential
Electric power distribution
Electric vehicle charging
Electric vehicles
Electronic devices
Internet
Mathematical model
power distribution
smart grids
TCPIP
Threshold voltage
Transportation systems
Voltage
Voltage control
Voltage measurement
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Transportation systems are undergoing a major transition with the integration of electric vehicles (EVs). However, due to increase in battery energy and charger power ratings, potential adverse effects on the distribution grid is a crucial issue to be addressed. Large voltage drops at charging nodes will deteriorate the quality of power service and cause unfair utilization of grid capacity among EV users. Safe and efficient operation of the grid along with a fast, convenient, and fair charging strategy is an important research problem. In this paper, we adapt the additive increase multiplicative decrease (AIMD) algorithm used in the Internet congestion control to EV charging using only local node measurements. We analyze the relationship between distance and grid voltage, and show how to extract this information from local measurements. Then, we present a detailed analysis to understand the relationship between distance and charging power in a distribution network to better address the fairness in the proposed AIMD EV charging algorithm. Results show that localized information at charging node voltages include important signature information on grid congestion and can be used to implement AIMD control for EV charging.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2019.2903835