Load Profile Simulations for Smart Charging of Electric Vehicles Considering Driving Behavior and Vehicle Performance

2021 ◽  
Author(s):  
Nattavit Piamvilai ◽  
Somporn Sirisumrannukul
Keyword(s):  
Electric Vehicles ◽  
Driving Behavior ◽  
Vehicle Performance ◽  
Load Profile ◽  
Smart Charging

scholarly journals Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4717 ◽  
Author(s):  
Sylvester Johansson ◽  
Jonas Persson ◽  
Stavros Lazarou ◽  
Andreas Theocharis
Keyword(s):  
Electric Vehicles ◽  
Power Distribution ◽  
Large Scale ◽  
Electrical Power ◽  
Distribution Network ◽  
Transport Sector ◽  
Smart Charging ◽  
Large Scale Integration ◽  
Scale Integration ◽  
The Impact

Social considerations for a sustainable future lead to market demands for electromobility. Hence, electrical power distribution operators are concerned about the real ongoing problem of the electrification of the transport sector. In this regard, the paper aims to investigate the large-scale integration of electric vehicles in a Swedish distribution network. To this end, the integration pattern is taken into consideration as appears in the literature for other countries and applies to the Swedish culture. Moreover, different charging power levels including smart charging techniques are examined for several percentages of electric vehicles penetration. Industrial simulation tools proven for their accuracy are used for the study. The results indicate that the grid can manage about 50% electric vehicles penetration at its current capacity. This percentage decreases when higher charging power levels apply, while the transformers appear overloaded in many cases. The investigation of alternatives to increase the grid’s capabilities reveal that smart techniques are comparable to the conventional re-dimension of the grid. At present, the increased integration of electric vehicles is manageable by implementing a combination of smart gird and upgrade investments in comparison to technically expensive alternatives based on grid digitalization and algorithms that need to be further confirmed for their reliability for power sharing and energy management.

scholarly journals Smart charging for electric vehicles to minimise charging cost

2017 ◽  
Vol 231 (6) ◽  
pp. 526-534 ◽  
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.

A Lightweight User Interface for Smart Charging of Electric Vehicles: A Real-World Application

2021 ◽  
Author(s):  
Stefan Meisenbacher ◽  
Karl Schwenk ◽  
Johannes Galenzowski ◽  
Simon Waczowicz ◽  
Ralf Mikut ◽  
...  
Keyword(s):  
User Interface ◽  
Electric Vehicles ◽  
Real World ◽  
Real World Application ◽  
Smart Charging

scholarly journals Evaluation of OCPP and IEC 61850 for Smart Charging Electric Vehicles

2013 ◽  
Vol 6 (4) ◽  
pp. 863-874 ◽  
Author(s):  
Jens Schmutzler ◽  
Claus Andersen ◽  
Christian Wietfeld
Keyword(s):  
Electric Vehicles ◽  
Iec 61850 ◽  
Smart Charging

Smart charging of electric vehicles – integration of energy and information

2014 ◽  
Vol 4 (4) ◽  
pp. 89-96 ◽  
Author(s):  
Ching Chuen Chan ◽  
Linni Jian ◽  
Dan Tu
Keyword(s):  
Electric Vehicles ◽  
Smart Charging

The Development of Remote Monitoring and Diagnostic System for Electric Vehicles

2014 ◽  
Vol 535 ◽  
pp. 125-130
Author(s):  
Wei Liu ◽  
Shi Chun Yang ◽  
Ming Li
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Remote Monitoring ◽  
Rapid Development ◽  
Diagnostic System ◽  
Area Network ◽  
Network Technology ◽  
Vehicle Performance ◽  
Information Monitoring ◽  
Low Energy Consumption

Electric vehicle, drove by motor, has the advantages of low energy consumption, zero emissions and high comfort. Nowadays, electric vehicle, at home and abroad, have a rapid development. However, we dont effective optimize the vehicle performance and the parts when vehicle is operating. Based on the GPS (Global Position System), 3G(The 3rd Generation Telecommunication), CAN(Controller Area Network) technology, this paper design the remote monitoring and diagnostic system. This paper makes some explanation on the design of remote monitoring and diagnostic system and the function of collecting information, monitoring, storage, fault diagnosis and alarm. Experimental show, this system has the characteristics about good interaction, high real-time, high precision and stability and so on, which can be widely used in vehicle remote monitoring.

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