Behavioral-alike model-driven residential charger power projection in serving private electric mobility in the new capital city of Indonesia

Abstract

The provision of electric charging facilities in public areas, public transport depots, logistics depots, logistics consignment hubs, and residential areas is targeted for Indonesia's new capital city (INCC) in pacing the (near to) zero-emission in the transport sector by 2045. Hence, the supportive electric mobility infrastructure should gradually be merged from the design stage to achieve directed goals. However, since the planning process for grid-supporting infrastructure is undergoing or within the preliminary phase or has not been done yet, there is a lack of required data, i.e., the rollout number of electric vehicles (EVs) units, sizing areas, and the compliance interconnection feeder. Meanwhile, using practical estimations or averaging techniques could lead to significant discrepancies, particularly regarding grid-supportive investment planning, i.e., redesign for grid capacity assets. On that concern, the paper constructs a model-driven using the predictive Monte Carlo method (MCM) to generate power load patterns, mimicking behavior-alike of residential home chargers for two|three|four wheeled EVs (2|3|4 WEVs) providing high-resolution power consumption data over unknown parameters related to the EVs properties, user behavior charging timeframes, and the expected tailor-made platform (sizing and allocation). Therefore, the findings are expected to be evidence-based replanning policy for policymakers, energy planners, and utilities.