Modeling public charging infrastructure considering points of interest and parking potentials

Evamaria Zauner,Jan Ulffers,Philip Gauglitz,David Geiger

doi:10.5194/adgeo-56-1-2021

Abstract

Abstract. Considering climate change, it is essential to reduce CO2 emissions. The provision of charging infrastructure in public spaces for electromobility – along with the substitution of conventional power generation by renewable energies – can contribute to the energy transition in the transport sector. Scenarios for the spatial distribution of this charging infrastructure can help to exemplify the need for charging points and their impact, for example on power grids. We model two kinds of demand for public charging infrastructure. First, we model the demand for public charging points to compensate for the lack of home charging points, which is derived from a previously developed and published model addressing electric-vehicle ownership (with and without home charging options) in households. Second, and in the focus of the work presented here, is the demand for public charging infrastructure at points of interest (POIs). Their locations are derived from OpenStreetMap (OSM) data and weighted based on an evaluation of movement profiles from the Mobilität in Deutschland survey (MiD, German for “Mobility in Germany”). We combine those two demands with the available parking spaces and generate distributions for possible future charging points. We use a raster-based approach in which all vector data are rasterized and computations are performed on a municipality's full grid. The presented application area is Wiesbaden, and the methodology is generally applicable to municipalities in Germany. The model is compared with three other models or model variants in a correlation comparison in order to determine the influence of certain model assumptions and input data. The identification of potential charging points in public spaces plays an important role in modeling the future energy system – especially the power grid – as the rapid adoption of electric vehicles will shift locations of electrical demand. With our investigation, we would like to present a new method to simulate future public charging point locations and show the influences of different modeling methods.

Highlights

The dynamic transition to electric vehicles offers the opportunity for significant CO2 reduction and presents challenges due to the need for charging infrastructure to be integrated into the electric grid (Gauglitz et al, 2020).In order to make the coming challenges for energy system technology in general and power grids in particular visible and manageable, numerous models and studies exist to map scenarios of future charging infrastructure
We presented a model to generate possible future distributions of charging points in public spaces
To investigate the influence of different model approaches, a model comparison was carried out in which four different models were compared in a correlation analysis

Summary

Introduction

The dynamic transition to electric vehicles offers the opportunity for significant CO2 reduction and presents challenges due to the need for charging infrastructure to be integrated into the electric grid (Gauglitz et al, 2020). In order to make the coming challenges for energy system technology in general and power grids in particular visible and manageable, numerous models and studies exist to map scenarios of future charging infrastructure. As mentioned in Gauglitz et al (2020), studies range from higher-level distributions using simple allocation variables such as population or vehicle density (Braun et al, 2018; Vopava et al, 2017; 50Hertz Transmission GmbH, Amprion gmbH, Tennet TSO GmbH, TransnetBW GmbH, 2018) to specific studies of individual application areas considering detailed local conditions such as the American state roads (Xu and Meng, 2020) or individual cities like Hamburg (Rothfuchs et al, 2018). A connection to traditional vehicle fleets and driving profiles is established in Bundesministerium für Verkehr und digitale Infrastruktur (2021).

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