Simulating Electric Vehicle Diffusion and Charging Activities in France and Germany

Ensslen, Axel; Will, Christian; Jochem, Patrick · 2019 · Crossref

DOI: 10.3390/wevj10040073

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Summary

This paper addresses the challenge of modeling Plug-in Electric Vehicle (PEV) diffusion, adoption, and charging behavior to support energy system analysis. The authors aim to create consistent scenarios that combine macro-level market penetration with micro-level individual driving and charging patterns, specifically for France and Germany. The motivation stems from the need to integrate detailed PEV load profiles into agent-based energy system models simulating European day-ahead electricity markets, while overcoming the high computational costs associated with modeling millions of individual vehicles. To achieve this, the study employs a hybrid modeling approach. First, a top-down macro-econometric Bass diffusion model estimates the total number of PEVs in the market over time, calibrated using historical stock data and government targets (e.g., 6 million PEVs by 2030). Second, a bottom-up micro-econometric binary logistic regression model determines individual adoption probabilities based on survey data from the Cross-border Mobility for Electric Vehicles (CROME) project. These probabilities are assigned to individuals in representative national mobility datasets (MiD 2008 for Germany and ENTD 2008 for France). To manage computational complexity, the authors introduce a re-sampling method (Method 2) that selects a reduced, representative subset of adopters and scales their energy demand to match the total population, significantly reducing simulation time. Charging behavior is simulated assuming vehicles charge at home or work, with parameters based on the Nissan Leaf, including a 60 kWh battery, 3.7 kW charging power, and a 100 km minimum range requirement. The results indicate that PEV diffusion dynamics are slightly higher in France than in Germany, driven by a higher innovation coefficient in the Bass model. Despite this, average plug-in times, active charging periods, and load-shifting potentials differ only slightly between the two countries. In the 2030 scenario, approximately 5.5 million German and 5.9 million French adopters charge their vehicles, averaging two charging events per day. The total daily energy charged is approximately 60 GWh for both nations. Flexible charging strategies shift load peaks to nighttime and noon hours, reducing evening peaks. The re-sampling method successfully reduces the sample size to roughly 20% of the original while maintaining accurate total energy demand figures, though minor deviations occur in other metrics like total energy directly charged. The significance of this work lies in its ability to integrate granular PEV behavioral data into large-scale energy system simulations without prohibitive computational costs. By validating the re-sampling approach, the authors demonstrate that detailed, bottom-up modeling of PEV diffusion and charging can be feasibly applied to simulate European wholesale electricity markets. This facilitates more accurate assessments of the impact of PEV adoption on power supply, infrastructure, and market prices, supporting policy and infrastructure planning for the electrification of transport.

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