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An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Author

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  • Tianze Lan

    (Hubei Power Grid Intelligent Control and Equipment Engineering Technology Research Center, Hubei University of Technology, Wuhan 430072, China)

  • Kittisak Jermsittiparsert

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Faculty of Humanities and Social Sciences, Duy Tan University, Da Nang 550000, Vietnam)

  • Sara T. Alrashood

    (Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia)

  • Mostafa Rezaei

    (Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan 4111, Brisbane, Australia)

  • Loiy Al-Ghussain

    (Mechanical Engineering Department, University of Kentucky, Lexington, KY 40506, USA)

  • Mohamed A. Mohamed

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt
    Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China)

Abstract

Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

Suggested Citation

  • Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:569-:d:485529
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    References listed on IDEAS

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    3. Gupta, S. & Maulik, A. & Das, D. & Singh, A., 2022. "Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
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    9. Angelos Patsidis & Adam Dyśko & Campbell Booth & Anastasios Oulis Rousis & Polyxeni Kalliga & Dimitrios Tzelepis, 2023. "Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods," Energies, MDPI, vol. 16(16), pages 1-19, August.
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    13. Surender Reddy Salkuti, 2023. "Advanced Technologies for Energy Storage and Electric Vehicles," Energies, MDPI, vol. 16(5), pages 1-7, February.
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    16. Khalid Alnowibet & Andres Annuk & Udaya Dampage & Mohamed A. Mohamed, 2021. "Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework," Sustainability, MDPI, vol. 13(21), pages 1-32, October.
    17. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    18. Maria Carmela Di Piazza, 2022. "Recent Developments and Trends in Energy Management Systems for Microgrids," Energies, MDPI, vol. 15(21), pages 1-6, November.
    19. Muhammad Anique Aslam & Syed Abdul Rahman Kashif & Muhammad Majid Gulzar & Mohammed Alqahtani & Muhammad Khalid, 2023. "A Novel Multi Level Dynamic Decomposition Based Coordinated Control of Electric Vehicles in Multimicrogrids," Sustainability, MDPI, vol. 15(16), pages 1-29, August.
    20. Dimitra G. Kyriakou & Fotios D. Kanellos, 2022. "Optimal Operation of Microgrids Comprising Large Building Prosumers and Plug-in Electric Vehicles Integrated into Active Distribution Networks," Energies, MDPI, vol. 15(17), pages 1-26, August.
    21. Nick Rigogiannis & Ioannis Bogatsis & Christos Pechlivanis & Anastasios Kyritsis & Nick Papanikolaou, 2023. "Moving towards Greener Road Transportation: A Review," Clean Technol., MDPI, vol. 5(2), pages 1-25, June.
    22. Yan Xiong & Jiakun Fang, 2022. "Co-Operative Optimization Framework for Energy Management Considering CVaR Assessment and Game Theory," Energies, MDPI, vol. 15(24), pages 1-17, December.

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