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Optimal Capacity and Charging Scheduling of Battery Storage through Forecasting of Photovoltaic Power Production and Electric Vehicle Charging Demand with Deep Learning Models

Author

Listed:
  • Fachrizal Aksan

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Vishnu Suresh

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Przemysław Janik

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

Abstract

The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure.

Suggested Citation

  • Fachrizal Aksan & Vishnu Suresh & Przemysław Janik, 2024. "Optimal Capacity and Charging Scheduling of Battery Storage through Forecasting of Photovoltaic Power Production and Electric Vehicle Charging Demand with Deep Learning Models," Energies, MDPI, vol. 17(11), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2718-:d:1407912
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    References listed on IDEAS

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    1. Gülsah Erdogan & Wiem Fekih Hassen, 2023. "Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations," Energies, MDPI, vol. 16(18), pages 1-29, September.
    2. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    3. Limouni, Tariq & Yaagoubi, Reda & Bouziane, Khalid & Guissi, Khalid & Baali, El Houssain, 2023. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, Elsevier, vol. 205(C), pages 1010-1024.
    4. Fachrizal Aksan & Yang Li & Vishnu Suresh & Przemysław Janik, 2023. "Multistep Forecasting of Power Flow Based on LSTM Autoencoder: A Study Case in Regional Grid Cluster Proposal," Energies, MDPI, vol. 16(13), pages 1-20, June.
    5. Yin, Wanjun & Ji, Jianbo, 2024. "Research on EV charging load forecasting and orderly charging scheduling based on model fusion," Energy, Elsevier, vol. 290(C).
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    Cited by:

    1. Lin Liu & Tianjian Wang & Xiuchao Zhu & Chenming Liu, 2025. "Advancing Power Supply Resilience: Optimized Transmission Line Retrofitting Through Deep Q-Learning Algorithm," Energies, MDPI, vol. 18(16), pages 1-28, August.
    2. Ling Miao & Ning Zhou & Jianwei Ma & Hao Liu & Jian Zhao & Xiaozhao Wei & Jingyuan Yin, 2025. "Current Status, Challenges and Future Perspectives of Operation Optimization, Power Prediction and Virtual Synchronous Generator of Microgrids: A Comprehensive Review," Energies, MDPI, vol. 18(13), pages 1-41, July.

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