Probabilistic Load Profile Model for Public Charging Infrastructure to Evaluate the Grid Load

Andreas Weiß, Florian Biedenbach and Mathias Müller
Additional contact information
Andreas Weiß: Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 Munich, Germany
Florian Biedenbach: Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 Munich, Germany
Mathias Müller: Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 Munich, Germany

Energies, 2022, vol. 15, issue 13, 1-28

Abstract: The shift toward electric mobility in Germany is a major component of the German climate protection program. In this context, public charging is growing in importance, especially in high-density urban areas, which causes an additional load on the distribution grid. In order to evaluate this impact and prevent possible overloads, realistic models are required. Methods for implementing such models and their application in the context of grid load are research topics that are only minorly addressed in the literature. This paper aims to demonstrate the entire process chain from the selection of a modelling method to the implementation and application of the model within a case study. Applying a stochastic approach, charging points are modelled via probabilities to determine the start of charging, plug-in duration, and charged energy. Subsequently, load profiles are calculated, integrated into an energy system model and applied in order to analyze the effects of a high density of public charging points on the urban distribution grid. The case study highlights a possible application of the implemented probabilistic load profile model, but also reveals its limitations. The primary results of this paper are the identification and evaluation of relevant criteria for modelling the load profiles of public charging points as well as the demonstration of the model and its comparison to real charging processes. By publishing the determined probabilities and the model for calculating the charging load profiles, a comprehensive tool is provided.

Keywords: public charging; electric vehicle charging; electric avenue; grid load; energy system modeling; charging point modeling; stochastic modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/13/4748/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/13/4748/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:13:p:4748-:d:850863

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().