Operation-driven modeling of depot charging stations for electric bus rapid transit (eBRT) power demand projection for the new capital city of Indonesia

Abstract

The use of electric mobility (e-mobility) for urban eBRT is expanding globally as more carbon abatement backend technologies are put into practice. Supportive electric power build-up capacity and adequacy readiness infrastructure must be accurately prepared or reconfigured to accelerate the sector's decarbonization pathway. However, due to the integration of new technologies, which has never been done before, the unavailability of historical data gathered from practical field data or artificially generated data is likely unavoidable. Meanwhile, using as-built data referring to other countries or common presumptions is becoming problematic due to the high possibility of mismatches and uncertainty variables of the diverse scenario, which could lead to oversights and, thus, investment misconduct. Therefore, we construct a model replicating operation-driven eBRT depot charging stations, which focuses on the uncertain domains related to the fleet's attributes, lane destination, and recharging interval based on the predictive Monte Carlo model (PMCM) to generate time series charging demand required in the early stage of infrastructure expansion within the logical and scientific acceptance. The findings provide insights for all relevant actors, i.e., grid planners, stakeholders, and operators, emphasizing the evidence-based research for climate mitigation action toward Indonesia's new capital city (INCC) 2045 target.