Assessment of electric vehicles concerning impacts, charging infrastructure with unidirectional and bidirectional chargers, and power flow comparisons

This study describes, applies, and compares three different approaches to integrate electric vehicles (EVs) in a cost-minimising electricity system investment model and a dispatch model. The approaches include both an aggregated vehicle representation and individual driving profiles of passenger EVs. The driving patterns of 426 randomly selected vehicles in Sweden were recorded between 30 and 73 days each and used as input to the electricity system model for the individual driving profiles. The main conclusion is that an aggregated vehicle representation gives similar results as when including individual driving profiles for most scenarios modelled. However, this study also concludes that it is important to represent the heterogeneity of individual driving profiles in electricity system optimisation models when: (i) charging infrastructure is limited to only the home location in regions with a high share of solar and wind power in the electricity system, and (ii) when addressing special research issues such as impact of vehicle-to-grid (V2G) on battery health status. An aggregated vehicle representation will, if the charging infrastructure is limited to only home location, over-estimate the V2G potential resulting in a higher share (up to 10 percentage points) of variable renewable electricity generation and an under-estimation of investments in both short- and long-term storage technologies.

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BMS-driven onsite insolation charging infrastructure for electric vehicles

10.1063/5.0031510 ◽

2020 ◽

Author(s):

Vinay Gupta ◽

Himanshu Priyadarshi ◽

Vishnu Goyal ◽

Kulwant Singh ◽

Ashish Shrivastava ◽

...

Keyword(s):

Electric Vehicles ◽

Charging Infrastructure

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A Tale of Two Entities

ACM Transactions on Internet of Things ◽

10.1145/3437258 ◽

2021 ◽

Vol 2 (2) ◽

pp. 1-21

Author(s):

Hossam ElHussini ◽

Chadi Assi ◽

Bassam Moussa ◽

Ribal Atallah ◽

Ali Ghrayeb

Keyword(s):

Power Flow ◽

Power Grid ◽

Communication Protocols ◽

Charging Infrastructure ◽

Electric Vehicle Charging ◽

Public Data ◽

Simulation Based ◽

Charging Stations ◽

The Impact ◽

Ev Charging

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.

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Electric vehicles and charging infrastructure in Turkey: An overview

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2021.110913 ◽

2021 ◽

Vol 143 ◽

pp. 110913

Author(s):

Ömer Gönül ◽

A. Can Duman ◽

Önder Güler

Keyword(s):

Electric Vehicles ◽

Charging Infrastructure

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Smart charging for electric vehicles to minimise charging cost

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650916688409 ◽

2017 ◽

Vol 231 (6) ◽

pp. 526-534 ◽

Cited By ~ 5

Author(s):

Yue Wang ◽

David Infield ◽

Simon Gill

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Distribution System ◽

Power Flow ◽

Optimal Power Flow ◽

Low Voltage ◽

Wholesale Price ◽

Heuristic Method ◽

Domain Model ◽

Smart Charging

This paper assumes a smart grid framework where the driving patterns for electric vehicles are known, time variations in electricity prices are communicated to householders, and data on voltage variation throughout the distribution system are available. Based on this information, an aggregator with access to this data can be employed to minimise electric vehicles charging costs to the owner whilst maintaining acceptable distribution system voltages. In this study, electric vehicle charging is assumed to take place only in the home. A single-phase Low Voltage (LV) distribution network is investigated where the local electric vehicles penetration level is assumed to be 100%. Electric vehicle use patterns have been extracted from the UK Time of Use Survey data with a 10-min resolution and the domestic base load is generated from an existing public domain model. Apart from the so-called real time price signal, which is derived from the electricity system wholesale price, the cost of battery degradation is also considered in the optimal scheduling of electric vehicles charging. A simple and effective heuristic method is proposed to minimise the electric vehicles’ charging cost whilst satisfying the requirement of state of charge for the electric vehicles’ battery. A simulation in OpenDSS over a period of 24 h has been implemented, taking care of the network constraints for voltage level at the customer connection points. The optimisation results are compared with those obtained using dynamic optimal power flow.

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