IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i7p1680-d1112966.html
   My bibliography  Save this article

Reinforcement Learning-Enabled Electric Vehicle Load Forecasting for Grid Energy Management

Author

Listed:
  • M. Zulfiqar

    (Department of Telecommunication Systems, Bahauddin Zakariya University, Multan 60000, Pakistan
    Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Nahar F. Alshammari

    (Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • M. B. Rasheed

    (Escuela Politécnica Superior, Universidad de Alcalá, ISG, 28805 Alcalá de Henares, Spain)

Abstract

Electric vehicles are anticipated to be essential components of future energy systems, as they possess the capability to assimilate surplus energy generated by renewable sources. With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), conventional internal combustion engine (ICE)-based vehicles are expected to be gradually phased out, thereby decreasing greenhouse gases and reliance on foreign oil. Intensive research and development efforts across the globe are currently concentrated on developing effective PHEV charging solutions that can efficiently cater to the charging needs of PHEVs, while simultaneously minimizing their detrimental effects on the power infrastructure. Efficient PHEV charging strategies and technologies are necessary to overcome the obstacles presented. Forecasting PHEV charging loads provides a solution by enabling energy delivery to power systems based on anticipated future loads. We have developed a novel approach, utilizing machine learning methods, for accurately forecasting PHEV charging loads at charging stations across three phases of powering (smart, non-cooperative, and cooperative). The proposed Q-learning method outperforms conventional AI techniques, such as recurrent neural and artificial neural networks, in accurately forecasting PHEV loads for various charging scenarios. The findings indicate that the Q-learning method effectively predicts PHEV loads in three scenarios: smart, non-cooperative, and cooperative. Compared to the ANN and RNN models, the forecast precision of the QL model is higher by 31.2% and 40.7%, respectively. The Keras open-source set was utilized to simulate three different approaches and evaluate the efficacy and worth of the suggested Q-learning technique.

Suggested Citation

  • M. Zulfiqar & Nahar F. Alshammari & M. B. Rasheed, 2023. "Reinforcement Learning-Enabled Electric Vehicle Load Forecasting for Grid Energy Management," Mathematics, MDPI, vol. 11(7), pages 1-20, March.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1680-:d:1112966
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/7/1680/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/7/1680/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weis, Allison & Jaramillo, Paulina & Michalek, Jeremy, 2014. "Estimating the potential of controlled plug-in hybrid electric vehicle charging to reduce operational and capacity expansion costs for electric power systems with high wind penetration," Applied Energy, Elsevier, vol. 115(C), pages 190-204.
    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. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    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. Bampos, Zafeirios N. & Laitsos, Vasilis M. & Afentoulis, Konstantinos D. & Vagropoulos, Stylianos I. & Biskas, Pantelis N., 2024. "Electric vehicles load forecasting for day-ahead market participation using machine and deep learning methods," Applied Energy, Elsevier, vol. 360(C).

    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. Yuan, Hong & Ma, Minda & Zhou, Nan & Xie, Hui & Ma, Zhili & Xiang, Xiwang & Ma, Xin, 2024. "Battery electric vehicle charging in China: Energy demand and emissions trends in the 2020s," Applied Energy, Elsevier, vol. 365(C).
    2. Zhong, Zewei & Zeng, Yun & Zhao, Xiaoli & Zhang, Sufang, 2024. "The social benefits resulting from electric vehicle smart charging balancing economy and decarbonization," Transport Policy, Elsevier, vol. 147(C), pages 113-124.
    3. Simolin, Toni & Rauma, Kalle & Viri, Riku & Mäkinen, Johanna & Rautiainen, Antti & Järventausta, Pertti, 2021. "Charging powers of the electric vehicle fleet: Evolution and implications at commercial charging sites," Applied Energy, Elsevier, vol. 303(C).
    4. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    5. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    6. Hegde, Bharatkumar & Ahmed, Qadeer & Rizzoni, Giorgio, 2020. "Velocity and energy trajectory prediction of electrified powertrain for look ahead control," Applied Energy, Elsevier, vol. 279(C).
    7. Dimitrios Loukatos & Vasileios Arapostathis & Christos-Spyridon Karavas & Konstantinos G. Arvanitis & George Papadakis, 2024. "Power Consumption Analysis of a Prototype Lightweight Autonomous Electric Cargo Robot in Agricultural Field Operation Scenarios," Energies, MDPI, vol. 17(5), pages 1-24, March.
    8. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    9. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).
    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. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    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. Szinai, Julia K. & Sheppard, Colin J.R. & Abhyankar, Nikit & Gopal, Anand R., 2020. "Reduced grid operating costs and renewable energy curtailment with electric vehicle charge management," Energy Policy, Elsevier, vol. 136(C).
    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. Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Tan, Kang Miao & Mithulananthan, N., 2015. "A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 365-385.
    17. Bert Willems & Juulia Zhou, 2020. "The Clean Energy Package and Demand Response: Setting Correct Incentives," Energies, MDPI, vol. 13(21), pages 1-19, October.
    18. 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.
    19. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    20. Amaro García-Suárez & José-Luis Guisado-Lizar & Fernando Diaz-del-Rio & Francisco Jiménez-Morales, 2021. "A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 13(10), pages 1-14, 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:jmathe:v:11:y:2023:i:7:p:1680-:d:1112966. 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.