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Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station

Author

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  • Namhyun Ahn

    (Department of Electronic Engineering, Sogang University, Seoul 04107, Korea)

  • So Yeon Jo

    (Department of Electronic Engineering, Sogang University, Seoul 04107, Korea)

  • Suk-Ju Kang

    (Department of Electronic Engineering, Sogang University, Seoul 04107, Korea)

Abstract

An increase in the number of electrical vehicles has resulted in an increase in the number of electrical vehicle charging stations. As a result, the electricity load consumed by charging stations has become large enough to de-stabilize the electricity supply system. Therefore, real-time monitoring of how much electricity each charging station is consuming has become very much important. However, only limited information such as charging time is available from the operators of electric vehicle charging stations. The actual electricity consumption data is not provided in real time. Conventional methods estimate the accumulated electricity consumption of charging stations using a linear regression curve. However, an estimate of the electricity consumption for each charge is needed. In this paper, we propose an advanced electricity estimation system which predicts the energy consumption for each charge. The proposed method uses a constraint-aware non-linear regression curve, and performs additional data selection processes. The experimental results show that the proposed system achieves about 73% regression accuracy. In addition, the proposed system can display the energy consumption per hour and visualize this information on a map. This makes it possible to monitor the electricity consumption of the charging stations in real-time and by location, which helps to select appropriate locations where new vehicle charging stations need to be installed.

Suggested Citation

  • Namhyun Ahn & So Yeon Jo & Suk-Ju Kang, 2019. "Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station," Energies, MDPI, vol. 12(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1000-:d:213986
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    References listed on IDEAS

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    Cited by:

    1. Munseok Chang & Sungwoo Bae & Gilhwan Cha & Jaehyun Yoo, 2021. "Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    2. Mihai Rata & Gabriela Rata & Constantin Filote & Maria Simona Raboaca & Adrian Graur & Ciprian Afanasov & Andreea-Raluca Felseghi, 2019. "The ElectricalVehicle Simulator for Charging Station in Mode 3 of IEC 61851-1 Standard," Energies, MDPI, vol. 13(1), pages 1-10, December.

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