Agent-Based Electromobility Simulation Framework for the Prospective Analysis of Electric Vehicles Energy Demand in Urban Areas

Othman, Bassel; De Nunzio, Giovanni · 2024 · Crossref

DOI: 10.1109/itsc58415.2024.10920130

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Summary

This paper presents a sequential, agent-based simulation framework designed to analyze the impact of mass electric vehicle (EV) adoption on urban mobility compatibility, energy demand, and environmental outcomes. The research addresses the need for detailed prospective analysis that goes beyond macroscopic models, which often fail to assess individual mobility compatibility or the nuanced interactions between synthetic agents and charging infrastructure. By decoupling mobility simulation from electromobility modeling, the framework allows for the efficient evaluation of multiple scenarios with varying EV penetration rates without rerunning computationally expensive mobility simulations. The methodology consists of three stages: mobility simulation, pre-processing, and electromobility simulation. First, the open-source MATSim framework, combined with the EQASim pipeline, generates synthetic daily activity chains for a population in the Greater Lyon area, France. The study analyzes over 600,000 agents who drive at least once daily, using a sampling rate of 1 to ensure data richness. Second, pre-processing models assign Home Charging Stations (HCS) and Work Charging Stations (WCS) based on household income, housing type, and job density, while an EV adoption model assigns vehicles to households based on income and housing characteristics. Third, the electromobility simulator tracks battery state of charge, applying a consumption rate of 160 Wh/km and a 41 kWh battery capacity. Charging behavior prioritizes home, then work, and finally public charging, with specific state-of-charge thresholds for each. The case study evaluates three scenarios—baseline, boosted HCS, and boosted WCS—across EV penetration rates from 5% to 50%. Results indicate that the boosted WCS scenario yields the highest EV compatibility, as workplace chargers allow for daily rotation among agents, unlike fixed home chargers. Energy demand analysis reveals that home charging remains the dominant source across all scenarios. However, public charging stations absorb an increasing share of energy demand as penetration rates rise, particularly when home and work infrastructure is limited. Peak power demand increases significantly, from 28 MW at 5% penetration to 204 MW at 50% penetration in the baseline scenario. The study also highlights the potential for peak shaving through workplace charging. Environmental impact assessments, using Life Cycle Assessment factors of 80 gCO2 eq/km for EVs versus 170 gCO2 eq/km for conventional vehicles, estimate that 100% EV adoption in the study area would reduce annual CO2 emissions by approximately 600,000 tonnes. The significance of this work lies in its efficient, decoupled meta-model approach, which enables decision-makers to test various infrastructure investment strategies and penetration rates under consistent travel demand conditions. The findings underscore the critical role of workplace charging in enhancing EV compatibility and managing grid load, while confirming that public infrastructure becomes increasingly vital at high adoption levels. The framework provides a scalable tool for assessing the readiness of urban areas for electromobility transition and identifying levers for sustainable transportation planning.

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