Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

Wulff, Niklas; Steck, Felix; Gils, Hans Christian; Hoyer-Klick, Carsten; Adel, Bent van den; Anderson, John E.

doi:10.3390/en13051093

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…Following 3 , we argue that new models are needed to derive relevant time series in a transparent and flexible way. As a first step in this direction, the tools Vencopy 17 and RAMP-mobility 18 recently emerged. To further fill this gap, we developed emobpy.…”

Section: Introductionmentioning

confidence: 99%

An open tool for creating battery-electric vehicle time series from empirical data, emobpy

et al. 2021

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There is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physical properties of battery-electric vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. For an illustration, we create and characterize 200 vehicle profiles for Germany. Depending on the hour of the day, a fleet of one million vehicles has a median grid availability between 5 and 7 gigawatts, as vehicles are parking most of the time. Four exemplary grid electricity demand time series illustrate the smoothing effect of balanced charging strategies.

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Section: Introductionmentioning

confidence: 99%

An open tool for creating battery-electric vehicle time series from empirical data, emobpy

et al. 2021

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“…As described above, several steps are taken in order to calculate average profiles for entire fleets. In this paper, no results for a scenario study of 2030 or similar, as done with VencoPy in [5], are shown but the interest is rather focused on all resulting profiles on the level of the average vehicle, thus providing a basis for scaling to any fleet size.…”

Section: Case Study Results: Mobility Patternsmentioning

confidence: 99%

“…Transport and energy systems analysis approaches have been combined and compared in [5] to analyze the effects of different model details and the respective implications on energy systems optimization model results.…”

Section: State Of Researchmentioning

confidence: 99%

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

et al. 2021

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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.

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“…In previous studies, REMix was used to estimate the cost-optimal design of energy systems and has been applied in several studies, ranging from case studies for specific regions [22][23][24][25][26][27], model comparisons [28] to comprehensive model coupling [29]. The model adaptions necessary to consider LCA-based indicators in the REMix are described in the next section.…”

Section: The Traditional Remix Modeling Frameworkmentioning

confidence: 99%

Considering Life Cycle Greenhouse Gas Emissions in Power System Expansion Planning for Europe and North Africa Using Multi-Objective Optimization

et al. 2021

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We integrate life cycle indicators for various technologies of an energy system model with high spatiotemporal detail and a focus on Europe and North Africa. Using multi-objective optimization, we calculate a pareto front that allows us to assess the trade-offs between system costs and life cycle greenhouse gas (GHG) emissions of future power systems. Furthermore, we perform environmental ex-post assessments of selected solutions using a broad set of life cycle impact categories. In a system with the least life cycle GHG emissions, the costs would increase by ~63%, thereby reducing life cycle GHG emissions by ~82% compared to the cost-optimal solution. Power systems mitigating a substantial part of life cycle GHG emissions with small increases in system costs show a trend towards a deployment of wind onshore, electricity grid and a decline in photovoltaic plants and Li-ion storage. Further reductions are achieved by the deployment of concentrated solar power, wind offshore and nuclear power but lead to considerably higher costs compared to the cost-optimal solution. Power systems that mitigate life cycle GHG emissions also perform better for most impact categories but have higher ionizing radiation, water use and increased fossil fuel demand driven by nuclear power. This study shows that it is crucial to consider upstream GHG emissions in future assessments, as they represent an inheritable part of total emissions in ambitious energy scenarios that, so far, mainly aim to reduce direct CO2 emissions.

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Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

Cited by 18 publications

References 23 publications

An open tool for creating battery-electric vehicle time series from empirical data, emobpy

An open tool for creating battery-electric vehicle time series from empirical data, emobpy

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

Considering Life Cycle Greenhouse Gas Emissions in Power System Expansion Planning for Europe and North Africa Using Multi-Objective Optimization

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