Spatial Modeling for Determining Electric Vehicle Charging Station Allocation in North Jakarta

Galih Prasetya Dinanta, Abdul Wachid Syamroni, Nugraheni Setyaningrum, Moch Arief Albachrony, Asih Kurniasari, Danang Yogisworo, Tisha A.A. Jamaluddin, Imaduddin Haq

Abstract


The rapid gradual shifting of electric vehicles (EVs) as a part of the green transition of electric mobility (e-mobility) has brought one of the most challenging issues for the urban planner on how to allocate the Public Electrical Vehicle Charging Station (PEVCS) for future urban ecosystems in a technical and economic viability achievement. This study employed the Neural Network method, which uses a supervised learning model to simulate a model with two essential parameters: the Origin to Destination (OD), which is modularly arranged over a square area of 5×5 kilometers per square, and the energy consumption prediction. The number of EV units is limited to twenty-five thousand for computation efficiency, and the study area is located in North Jakarta, Indonesia. Markov Chain Model (MCM) and Monte Carlo Simulation (MCS) were applied in the simulation. The electric power grid as a supply point of connection (PoC) was included in the computation. The proposed model aims to bridge the gap between the demand requirements of PEVCS based on the commuting behavior of EV users and the charging point on the availability of existing supportive grid infrastructure toward urban landscapes and innate traffic nature, especially in the initial transitioning of developing countries which are different compare with Europe, China, and the United States. On the basis of the model traffic density, battery consumption, proximity distance between the supporting grid feeder, and on-the-go user behavior, it is recommended that the PEVCS is suitably allocated in the commercial area 66.6 % out of 33.3 % for residential areas.

Keywords


Electric Vehicles, Charging Station Placement, Artificial Neural Network, Spatial Modeling, travel distance, point of connection.

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v13i2.13892.g8726

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