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

• Main Authors: Biedenbach, F. (Author), Müller, M. (Author), Weiß, A. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Charging (batteries); charging point modeling; Charging point modeling; electric avenue; Electric avenue; electric vehicle charging; Electric vehicle charging; energy system modeling; Energy-system models; grid load; Grid load; Load profile modeling; Point models; Probabilistic load; public charging; Public charging; stochastic modeling; Stochastic models; Stochastic systems; Stochastic-modeling
• Online Access: View Fulltext in Publisher

 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.