Multi-criteria optimization of electric vehicle fleet charging and discharging schedule for secondary frequency control

2019 ◽  
pp. 85-109
Author(s):  
Aleksandar Janjic ◽  
Lazar Z. Velimirovic
Keyword(s):  
Electric Vehicle ◽  
Frequency Control ◽  
Vehicle Fleet ◽  
Secondary Frequency Control

Commercial electric vehicle fleet scheduling for secondary frequency control

2017 ◽  
Vol 147 ◽  
pp. 31-41 ◽  
Author(s):  
Aleksandar Janjic ◽  
Lazar Velimirovic ◽  
Miomir Stankovic ◽  
Andrija Petrusic
Keyword(s):  
Electric Vehicle ◽  
Frequency Control ◽  
Vehicle Fleet ◽  
Secondary Frequency Control

scholarly journals Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4349
Author(s):  
Niklas Wulff ◽  
Fabia Miorelli ◽  
Hans Christian Gils ◽  
Patrick Jochem
Keyword(s):  
Open Source ◽  
Electric Vehicle ◽  
Full Range ◽  
Mobility Patterns ◽  
Electric Vehicle Charging ◽  
Survey Results ◽  
Vehicle Demand ◽  
Vehicle Fleet ◽  
Electric Loads

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

scholarly journals The remaining CO2 budget: a comparison of the CO2 emissions of diesel and BEV drivetrain technology

2021 ◽  
Author(s):  
Christian Böhmeke ◽  
Thomas Koch
Keyword(s):  
Electric Vehicle ◽  
Carbon Footprint ◽  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Battery Electric Vehicle ◽  
Useful Life ◽  
Vehicle Fleet ◽  
Real Time Simulations ◽  
Diesel Vehicle

AbstractThis paper describes the CO2 emissions of the additional electricity generation needed in Germany for battery electric vehicles. Different scenarios drawn up by the transmission system operators in past and for future years for expansion of the energy sources of electricity generation in Germany are considered. From these expansion scenarios, hourly resolved real-time simulations of the different years are created. Based on the calculations, it can be shown that even in 2035, the carbon footprint of a battery electric vehicle at a consumption of 22.5 kWh/100 km including losses and provision will be around 100 g CO2/km. Furthermore, it is shown why the often-mentioned German energy mix is not suitable for calculating the emissions of a battery electric vehicle fleet. Since the carbon footprint of a BEV improves significantly over the years due to the progressive expansion of renewable-energy sources, a comparison is drawn at the end of this work between a BEV (29.8 tons of CO2), a conventional diesel vehicle (34.4 tons of CO2), and a diesel vehicle with R33 fuel (25.8 tons of CO2) over the entire useful life.

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