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Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

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  • Gülsah Erdogan

    (Chair of Distributed Information Systems, University of Passau, Innstraße 41, 94032 Passau, Germany)

  • Wiem Fekih Hassen

    (Chair of Distributed Information Systems, University of Passau, Innstraße 41, 94032 Passau, Germany)

Abstract

The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6656-:d:1241479
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    References listed on IDEAS

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    1. Vishnu Suresh & Przemyslaw Janik & Jacek Rezmer & Zbigniew Leonowicz, 2020. "Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm," Energies, MDPI, vol. 13(3), pages 1-15, February.
    2. Gianluca Bontempi & Souhaib Ben Taieb & Yann-Aël Le Borgne, 2013. "Machine learning strategies for time series forecasting," ULB Institutional Repository 2013/167761, ULB -- Universite Libre de Bruxelles.
    3. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Abdou, Ahmed Fathi, 2019. "Modified PSO algorithm for real-time energy management in grid-connected microgrids," Renewable Energy, Elsevier, vol. 136(C), pages 746-757.
    4. Seung-Ju Lee & Yourim Yoon, 2020. "Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming," Mathematics, MDPI, vol. 8(9), pages 1-20, September.
    5. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    6. Yazhou Zhao & Xiangxi Qin & Xiangyu Shi, 2022. "A Comprehensive Evaluation Model on Optimal Operational Schedules for Battery Energy Storage System by Maximizing Self-Consumption Strategy and Genetic Algorithm," Sustainability, MDPI, vol. 14(14), pages 1-34, July.
    7. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
    8. Gaurav Chaudhary & Jacob J. Lamb & Odne S. Burheim & Bjørn Austbø, 2021. "Review of Energy Storage and Energy Management System Control Strategies in Microgrids," Energies, MDPI, vol. 14(16), pages 1-26, August.
    9. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    10. Sheik Mohammed S. & Femin Titus & Sudhakar Babu Thanikanti & Sulaiman S. M. & Sanchari Deb & Nallapaneni Manoj Kumar, 2022. "Charge Scheduling Optimization of Plug-In Electric Vehicle in a PV Powered Grid-Connected Charging Station Based on Day-Ahead Solar Energy Forecasting in Australia," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    11. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
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    Cited by:

    1. 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.
    2. Imen Azzouz & Wiem Fekih Hassen, 2023. "Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach," Energies, MDPI, vol. 16(24), pages 1-18, December.
    3. Wiem Fekih Hassen & Maher Challouf, 2024. "Long Short-Term Renewable Energy Sources Prediction for Grid-Management Systems Based on Stacking Ensemble Model," Energies, MDPI, vol. 17(13), pages 1-19, June.
    4. Md Jamal Ahmed Shohan & Md Maidul Islam & Sophia Owais & Md Omar Faruque, 2024. "Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm," Energies, MDPI, vol. 17(21), pages 1-20, October.

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