Economic Placement of EV Charging Stations within Urban Areas

Author(s):  
Ahmed Ibrahim AbdelAzim
Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5441
Author(s):  
Leonidas Anthopoulos ◽  
Polytimi Kolovou

Electro-mobility (EV) is an emerging transportation method, whose charging infrastructure development concerns a key-factor for its growth. EV charging infrastructure has not grown yet in Greece, regardless of the ambitious national targets that have been grounded for 2030 towards a climate-neutral mobility. This study introduces a multi-criteria decision-making (MCDM) framework for EV charging infrastructure deployment and operation, which respects both the economic and the technical aspects for public charging stations. The analytic hierarchy process (AHP) was followed for the MCDM framework’s definition, which used criteria that were in the corresponding literature and performed with interviews by experts from the EV growing market in Greece. The results show that the installation and operation of public EV charging stations, located in private spaces to ensure their protection against vandalism, within the urban areas is the preferred deployment approach. Moreover, this article tests a market model for the EV charging infrastructure ownership and operation. Findings show that the incentive for investment in EV charging infrastructure market in Greece, is driven by the direct investments of limited vendors, while it is not economically oriented, but it focuses on sustainability and environmental protection.


Author(s):  
Hossein Parastvand ◽  
Octavian Bass ◽  
Mohammad A. S. Masoum ◽  
Zeinab Moghaddam ◽  
Stefan Lachowicz ◽  
...  

2021 ◽  
Vol 199 ◽  
pp. 107391
Author(s):  
Leonardo Bitencourt ◽  
Tiago P. Abud ◽  
Bruno H. Dias ◽  
Bruno S.M.C. Borba ◽  
Renan S. Maciel ◽  
...  

2021 ◽  
Vol 2 (2) ◽  
pp. 1-21
Author(s):  
Hossam ElHussini ◽  
Chadi Assi ◽  
Bassam Moussa ◽  
Ribal Atallah ◽  
Ali Ghrayeb

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.


AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 135-149
Author(s):  
James Flynn ◽  
Cinzia Giannetti

With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.


2021 ◽  
Author(s):  
T. Muthamizhan ◽  
M.Jagadeesh Kumar ◽  
P. Rathnavel ◽  
Md. Aijaz ◽  
A. Sivakumar

2021 ◽  
Author(s):  
Michele Barbieri ◽  
Massimo Ceraolo ◽  
Giovanni Lutzemberger ◽  
Davide Poli

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