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

Abstract

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.

Accurate and scalable representation of electric vehicles in energy system models: A virtual storage-based aggregation approach

“…By contrast, current plug-in rates for public charging are lower, especially during daytime, and this leads to less charging during the day and higher evening peaks in empirically observed charging profiles. 29 , 39 We simulate the effect of reduced plug-in rates for public charging in Note S3 , finding a peak load increase of 11%. Yet the power sector implications of this charging behavior are less clear and should be investigated in detail with dedicated energy models.…”

“… 25 , 26 , 27 , 28 Previous studies discuss that existing aggregation approaches used for energy system modeling overestimate the aggregated flexibility potential. 29 Although other studies have proposed more accurate aggregation algorithms for the optimization of EV charging, these are based on exogenous steering signals, such as prices or volume schedules. 28 , 30 , 31 Consequently, they cannot be integrated into energy system models where the dispatch of EVs is jointly optimized with the dispatch of other technologies and prices are determined endogenously.…”

Social Acceptance and Preference of EV Users—A Review

“…It is also possible to take advantage of home charging in cold weather conditions, as the vehicles can be preheated in the morning to avoid using the battery capacity for heating purposes. Social studies carried out in China [55] showed that the EV charging service has a great influence on consumers' decisions, as it depended on the level of the service provision and speed of service in public stations. Also, the availability of a home charging facility played a big role in consumers' decision to adopt EVs.…”

“…However, apart from the obvious, there is a case in [48], whereby the Italian consumers are starting to get convinced on the driving range which proves the WTP for extended 1-km driving range is lower. Otherwise, a free EV license, speed and battery swapping are also being spoken of as a part of WTP reward such in [55], [68], and [81]. For example, a substantial number of respondents are WTP more for battery swapping functionality [81].…”

Electric Vehicles Load Profile Generator Based on the Probability Density Functions

“…The possible impacts on LVDG must be modelled using simulation software in a simplified and transparent manner, considering limited information from the investigated grid: topology of the grid, customers and their profile type, available and perspective PV and EV charging pole connections. Existing approaches and open-source tools available in the literature: emobpy [12], VencoPy [13] and RAMP-mobility [14], provides comprehensive sources of country-wise information and requires specific information, which is not always available on a local level. Therefore, the main goal of this paper is to present a prototype of an Electric Vehicles Load Profile Generator (EVLPG) based on the probability density function (PDFs) of several parameters derived from previous tools or regional real charging events, such as arrival time during weekdays and weekends, connection time during weekdays and weekends and energy demand with information about battery size of the vehicle in charge and the power level of the charger for further integration of generated patterns in a defined LVDG by means of the open-source and commercial software's.…”