Published November 15, 2016 | Version v1
Journal article

A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas

Description

Highlights: • A demand-side approach to the location of charging infrastructure problem is discussed in the paper. • The analysis is based on a large data-set of private vehicle travels within the urban area of Rome. • Cluster analysis is applied to the data to find the optimal location zones for charging infrastructures. • The daily energy demand and the average number of users per day are calculated for each and every charging infrastructure. - Abstract: Despite all the acknowledged advantages in terms of environmental impact reduction, energy efficiency and noise reduction, the electric mobility market is below expectations. In fact, electric vehicles have limitations that pose several important challenges for achieving a sustainable mobility system: among them, the availability of an adequate charging infrastructure is recognized as a fundamental requirement and appropriate approaches to optimize public and private investments in this field are to be delineated. In this paper we consider actual data on conventional private vehicle usage in the urban area of Rome to carry out a strategy for the optimal allocation of charging infrastructures into portions (subareas) of the urban area, based on an analysis of a driver sample under the assumption of a complete switch to an equivalent fleet of electric vehicles. Moreover, the energy requirement for each one of the subareas is estimated in terms of the electric energy used by the equivalent fleet of electric vehicles to reach their destination. The model can be easily generalized to other problems regarding facility allocation based on user demand.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.apenergy.2016.07.137

Additional details

Identifiers

DOI
10.1016/j.apenergy.2016.07.137;
PII
S0306-2619(16)31087-X;

Publishing Information

Journal Title
Applied Energy
Journal Volume
182
Journal Page Range
p. 39-46
ISSN
0306-2619
CODEN
APENDX

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
48082493
Subject category
S29: ENERGY PLANNING, POLICY AND ECONOMY;
Descriptors DEI
CLUSTER ANALYSIS; ELECTRIC-POWERED VEHICLES; ENERGY DEMAND; ENERGY EFFICIENCY; ENVIRONMENTAL IMPACTS; PRIVATE VEHICLES; URBAN AREAS
Descriptors DEC
DATA ANALYSIS; DATA PROCESSING; DEMAND; EFFICIENCY; PROCESSING; TRANSPORTATION SYSTEMS; VEHICLES

Optional Information

Copyright
Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.