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Optimal Control Scheme of Electric Vehicle Charging Using Combined Model of XGBoost and Cumulative Prospect Theory

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  • Youseok Lim

    (Korea Electric Power Corporation, Daejeon 34056, Republic of Korea
    Department of Artificial Intelligence, Hanyang University, Seoul 04763, Republic of Korea)

  • Sungwoo Bae

    (Department of Electrical Engineering, Hanyang University, Seoul 04763, Republic of Korea)

  • Jun Moon

    (Department of Artificial Intelligence, Hanyang University, Seoul 04763, Republic of Korea)

Abstract

In this paper, we propose the XPaC (XGBoost Prediction and Cumulative Prospect Theory (CPT)) model to minimize the operational losses of the power grid, taking into account both the prediction of electric vehicle (EV) charging demand and the associated uncertainties, such as when customers will charge, how much electric energy they will need, and for how long. Given that power utilities supply electricity with limited resources, it is crucial to efficiently control EV charging peaks or predict charging demand during specific periods to maintain stable grid operations. While the total amount of EV charging is a key factor, when and where the charging occurs can be even more critical for the effective management of the grid. Although numerous studies have focused on individually predicting EV charging patterns or demand and evaluating the effectiveness of EV charging control, comprehensive assessments of the actual operational benefits and losses resulting from charging control based on predicted charging behavior remain limited. In this study, we firstly compare the performance of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and decision tree-based XGBoost regression models in predicting hourly charging probabilities and the need for grid demand control. Using the predicted results, we applied the CPT algorithm to analyze the optimal operational scenarios and assess the expected profit and loss for the power grid. Since the charging control optimizer with XPaC incorporates real-world operational data and uses actual records for analysis, it is expected to provide a robust solution for managing the demand arising from the rapid growth of electric vehicles, while operating within the constraints of limited energy resources.

Suggested Citation

  • Youseok Lim & Sungwoo Bae & Jun Moon, 2024. "Optimal Control Scheme of Electric Vehicle Charging Using Combined Model of XGBoost and Cumulative Prospect Theory," Energies, MDPI, vol. 17(24), pages 1-24, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6457-:d:1549715
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    References listed on IDEAS

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    1. Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    2. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
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