IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3498-d772636.html
   My bibliography  Save this article

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

Author

Listed:
  • Sheik Mohammed S.

    (Electrical and Electronic Engineering Programme Area, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei)

  • Femin Titus

    (Department of Electrical and Electronics Engineering, TKM College of Engineering, Kollam 691005, Kerala, India)

  • Sudhakar Babu Thanikanti

    (Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, Telangana, India)

  • Sulaiman S. M.

    (Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur 626128, Tamil Nādu, India)

  • Sanchari Deb

    (School of Engineering, University of Warwick, Coventry CV4 7AL, UK)

  • Nallapaneni Manoj Kumar

    (School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong)

Abstract

Optimal charge scheduling of electric vehicles in solar-powered charging stations based on day-ahead forecasting of solar power generation is proposed in this paper. The proposed algorithm’s major objective is to schedule EV charging based on the availability of solar PV power to minimize the total charging costs. The efficacy of the proposed algorithm is validated for a small-scale system with a capacity of 3.45 kW and a single charging point, and the annual cost analysis is carried out by modelling a 65 kWp solar-powered EV charging station The reliability and cost saving of the proposed optimal scheduling algorithm along with the integration and the solar PV system is validated for a charging station with a 65 kW solar PV system having charging points with different charging powers. A comprehensive comparison of uncontrolled charging, optimal charging without solar PV system, and optimal charging with solar PV system for different vehicles and different time slots are presented and discussed. From the results, it can be realized that the proposed charging algorithm reduces the overall charging cost from 10–20% without a PV system, and while integrating a solar PV system with the proposed charging method, a cost saving of 50–100% can be achieved. Based on the selected location, system size, and charging points, it is realized that the annual charging cost under an uncontrolled approach is AUS $28,131. On the other hand, vehicle charging becomes completely sustainable with net-zero energy consumption from the grid and net annual revenue of AUS $28,134.445 can be generated by the operator. New South Wales (NSW), Australia is selected as the location for the study. For the analysis Time-Of-Use pricing (ToUP) scheme and solar feed-in tariff of New South Wales (NSW), Australia is adopted, and the daily power generation of the PV system is computed using the real-time data on an hourly basis for the selected location. The power forecasting is carried out using an ANN-based forecast model and is developed using MATLAB and trained using the Levenberg–Marquardt algorithm. Overall, a prediction accuracy of 99.61% was achieved using the selected algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3498-:d:772636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3498/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3498/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jose Manuel Barrera & Alejandro Reina & Alejandro Maté & Juan Carlos Trujillo, 2020. "Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
    2. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    3. Guozhong Liu & Li Kang & Zeyu Luan & Jing Qiu & Fenglei Zheng, 2019. "Charging Station and Power Network Planning for Integrated Electric Vehicles (EVs)," Energies, MDPI, vol. 12(13), pages 1-22, July.
    4. Ghotge, Rishabh & van Wijk, Ad & Lukszo, Zofia, 2021. "Off-grid solar charging of electric vehicles at long-term parking locations," Energy, Elsevier, vol. 227(C).
    5. Nunes, Pedro & Figueiredo, Raquel & Brito, Miguel C., 2016. "The use of parking lots to solar-charge electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 679-693.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. Simone Balmelli & Francesco Moresino, 2023. "Coordination of Plug-In Electric Vehicle Charging in a Stochastic Framework: A Decentralized Tax/Incentive-Based Mechanism to Reach Global Optimality," Mathematics, MDPI, vol. 11(4), pages 1-24, February.
    3. Yang, Zhichun & Yang, Fan & Min, Huaidong & Tian, Hao & Hu, Wei & Liu, Jian & Eghbalian, Nasrin, 2023. "Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources," Energy, Elsevier, vol. 263(PA).
    4. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yin, Rumeng & He, Jiang, 2023. "Design of a photovoltaic electric bike battery-sharing system in public transit stations," Applied Energy, Elsevier, vol. 332(C).
    2. Yap, Kah Yung & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2022. "Solar Energy-Powered Battery Electric Vehicle charging stations: Current development and future prospect review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    3. Wu, Jiabin & Li, Qihang & Bie, Yiming & Zhou, Wei, 2024. "Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm," Energy, Elsevier, vol. 304(C).
    4. Simon Steinschaden & José Baptista, 2020. "Development of an Efficient Tool for Solar Charging Station Management for Electric Vehicles," Energies, MDPI, vol. 13(11), pages 1-21, June.
    5. Ghotge, Rishabh & van Wijk, Ad & Lukszo, Zofia, 2021. "Off-grid solar charging of electric vehicles at long-term parking locations," Energy, Elsevier, vol. 227(C).
    6. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    7. Vitalii Naumov, 2021. "Substantiation of Loading Hub Location for Electric Cargo Bikes Servicing City Areas with Restricted Traffic," Energies, MDPI, vol. 14(4), pages 1-16, February.
    8. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    9. Guangying Jin & Wei Feng & Qingpu Meng, 2022. "Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization," Sustainability, MDPI, vol. 14(21), pages 1-24, October.
    10. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    11. Huachun Han & Huiyu Miu & Shukang Lv & Xiaodong Yuan & Yi Pan & Fei Zeng, 2024. "Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge," Energies, MDPI, vol. 17(18), pages 1-21, September.
    12. Varone, Alberto & Heilmann, Zeno & Porruvecchio, Guido & Romanino, Alessandro, 2024. "Solar parking lot management: An IoT platform for smart charging EV fleets, using real-time data and production forecasts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    13. Secinaro, Silvana & Calandra, Davide & Lanzalonga, Federico & Ferraris, Alberto, 2022. "Electric vehicles’ consumer behaviours: Mapping the field and providing a research agenda," Journal of Business Research, Elsevier, vol. 150(C), pages 399-416.
    14. Deshmukh, Swaraj Sanjay & Pearce, Joshua M., 2021. "Electric vehicle charging potential from retail parking lot solar photovoltaic awnings," Renewable Energy, Elsevier, vol. 169(C), pages 608-617.
    15. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).
    16. Emilia M. Szumska & Rafał S. Jurecki, 2021. "Parameters Influencing on Electric Vehicle Range," Energies, MDPI, vol. 14(16), pages 1-23, August.
    17. Ruifeng Shi & Jiahua Liu & Zhenhong Liao & Li Niu & Eke Ibrahim & Fang Fu, 2019. "An Electric Taxi Charging Station Planning Scheme Based on an Improved Destination Choice Method," Energies, MDPI, vol. 12(19), pages 1-21, October.
    18. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    19. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    20. Despoina Kothona & Aggelos S. Bouhouras, 2022. "A Two-Stage EV Charging Planning and Network Reconfiguration Methodology towards Power Loss Minimization in Low and Medium Voltage Distribution Networks," Energies, MDPI, vol. 15(10), pages 1-17, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3498-:d:772636. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.