Bridging Transport and Energy Modelling: Deriving Residential Building Occupancy and BEV Charging Demand from Agent-Based Transport Models for Neighborhood-Level Energy Planning

Abstract

As the world aims to shift towards a low-carbon economy, the interaction of the transport and energy sectors is becoming increasingly critical. The rise of electrified mobility and the potential of Vehicle-to-Grid (V2G) technology, as well as increasing shares of energy from renewable sources, with varying production rate, change mobility systems and put electricity networks to a challenge. Despite the potential of models bridging the transport and energy sectors, their use is still limited. This paper addresses this gap by presenting a new framework that combines traffic and energy modeling. Our framework consists of an agent-based travel demand model (TAPAS) coupled to a model of battery electric vehicle (BEV) charging behavior (CHARGIN). By aggregating the agents' stays at locations between rides, we compute occupancy rates of residential buildings, and by passing rides and parking periods to the charging behavior model, we estimate tempo-spatial charging patterns and electricity demand from BEV charging for each time of the day for every building individually. We apply this framework to a case study in the district of Moabit, Berlin, providing valuable data for neighborhood energy optimization schemes. The proposed framework can be used for forecasting, scenario analysis, demand optimization, and V2G technology assessment, ultimately contributing to a more efficient and sustainable energy system.