EURO 2025 Leeds
Abstract Submission

2579. MAPPING THE MARKET DIFFUSION OF PRIVATE EV CHARGING IN GERMANY: A SPATIOTEMPORAL MODEL CONSIDERING REGIONAL ECONOMIC INCENTIVES

Invited abstract in session TB-46: Tackling Energy Problems with ML and Scarce Data, stream Energy Economics & Management.

Tuesday, 10:30-12:00
Room: Newlyn 1.07

Authors (first author is the speaker)

1. Reinhard Madlener
School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University
2. Kagan Yüksel
School of Business and Economics, RWTH Aachen University
3. Matthias Nagel
RWTH Aachen University

Abstract

Germany’s transportation sector is responsible for roughly one-fifth of the country’s greenhouse gas emissions, predominantly from passenger vehicles. Expanding electric mobility is therefore pivotal, yet growth is hindered by limited charging infrastructure—especially private stations that remain understudied due to data scarcity.Employing an Operations Research perspective, we frame the design and placement of private charging stations as a constrained resource allocation problem subject to uncertainties in demand, cost considerations, and spatial limitations. Advanced machine learning methods help address gaps in the underlying data and refine our decision-making framework. Specifically, a Spatio-Temporal Graph Convolution Network is used to integrate annual public charger data, housing characteristics, and socioeconomic factors. This approach, combined with adjacency-based cluster detection and scenario analyses (e.g., varying electricity prices and subsidies), infers private-charger adoption patterns and identifies critical drivers of growth. Results emphasize the significance of targeted infrastructure investments and supportive policies to alleviate range anxiety and spur home-based EV uptake. By yielding data-driven insights into charging station dynamics under sparse information, our study guides policymakers in designing strategies aligned with Germany’s climate targets and the evolving landscape of electric mobility.

Keywords

Status: accepted

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