lynx   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p8112-d1301872.html
   My bibliography  Save this article

Smart Decentralized Electric Vehicle Aggregators for Optimal Dispatch Technologies

Author

Listed:
  • Ali M. Eltamaly

    (Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia
    Saudi Electricity Company Research Chair in Power System Reliability and Security, Electric Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract
The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners’ disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid.

Suggested Citation

  • Ali M. Eltamaly, 2023. "Smart Decentralized Electric Vehicle Aggregators for Optimal Dispatch Technologies," Energies, MDPI, vol. 16(24), pages 1-27, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8112-:d:1301872
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/8112/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/24/8112/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eltamaly, Ali M., 2021. "A novel musical chairs algorithm applied for MPPT of PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    2. Han, Sekyung & Han, Soohee & Aki, Hirohisa, 2014. "A practical battery wear model for electric vehicle charging applications," Applied Energy, Elsevier, vol. 113(C), pages 1100-1108.
    3. Petit, Martin & Prada, Eric & Sauvant-Moynot, Valérie, 2016. "Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime," Applied Energy, Elsevier, vol. 172(C), pages 398-407.
    4. Marongiu, Andrea & Roscher, Marco & Sauer, Dirk Uwe, 2015. "Influence of the vehicle-to-grid strategy on the aging behavior of lithium battery electric vehicles," Applied Energy, Elsevier, vol. 137(C), pages 899-912.
    5. Ali M. Eltamaly & Mohamed A. Ahmed, 2023. "Performance Evaluation of Communication Infrastructure for Peer-to-Peer Energy Trading in Community Microgrids," Energies, MDPI, vol. 16(13), pages 1-16, July.
    6. Ali M. Eltamaly & Majed A. Alotaibi & Abdulrahman I. Alolah & Mohamed A. Ahmed, 2021. "IoT-Based Hybrid Renewable Energy System for Smart Campus," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    7. Crozier, Constance & Morstyn, Thomas & Deakin, Matthew & McCulloch, Malcolm, 2020. "The case for Bi-directional charging of electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 259(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. Zeyad A. Almutairi & Ali M. Eltamaly, 2024. "Synergistic Effects of Energy Storage Systems and Demand-Side Management in Optimizing Zero-Carbon Smart Grid Systems," Energies, MDPI, vol. 17(22), pages 1-32, November.

    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. Mathieu, Romain & Baghdadi, Issam & Briat, Olivier & Gyan, Philippe & Vinassa, Jean-Michel, 2017. "D-optimal design of experiments applied to lithium battery for ageing model calibration," Energy, Elsevier, vol. 141(C), pages 2108-2119.
    2. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Zaman, Forhad & Guerrero, Josep M., 2019. "Energy scheduling of community microgrid with battery cost using particle swarm optimisation," Applied Energy, Elsevier, vol. 254(C).
    3. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    4. Falahati, Saber & Taher, Seyed Abbas & Shahidehpour, Mohammad, 2016. "Grid frequency control with electric vehicles by using of an optimized fuzzy controller," Applied Energy, Elsevier, vol. 178(C), pages 918-928.
    5. Gschwendtner, Christine & Sinsel, Simon R. & Stephan, Annegret, 2021. "Vehicle-to-X (V2X) implementation: An overview of predominate trial configurations and technical, social and regulatory challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    6. Adnane Houbbadi & Rochdi Trigui & Serge Pelissier & Eduardo Redondo-Iglesias & Tanguy Bouton, 2019. "Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging," Energies, MDPI, vol. 12(14), pages 1-17, July.
    7. Petit, Martin & Prada, Eric & Sauvant-Moynot, Valérie, 2016. "Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime," Applied Energy, Elsevier, vol. 172(C), pages 398-407.
    8. Yohwan Choi & Hongseok Kim, 2016. "Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost," Energies, MDPI, vol. 9(6), pages 1-19, June.
    9. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    10. Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
    11. Sara Khan & Uzma Amin & Ahmed Abu-Siada, 2024. "P2P Energy Trading of EVs Using Blockchain Technology in Centralized and Decentralized Networks: A Review," Energies, MDPI, vol. 17(9), pages 1-17, April.
    12. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    13. Niu, Jide & Li, Xiaoyuan & Tian, Zhe & Yang, Hongxing, 2024. "Uncertainty analysis of the electric vehicle potential for a household to enhance robustness in decision on the EV/V2H technologies," Applied Energy, Elsevier, vol. 365(C).
    14. Xingtao Liu & Chaoyi Zheng & Ji Wu & Jinhao Meng & Daniel-Ioan Stroe & Jiajia Chen, 2020. "An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries," Energies, MDPI, vol. 13(2), pages 1-16, January.
    15. Song, Yuguang & Xia, Mingchao & Yang, Liu & Chen, Qifang & Su, Su, 2023. "Multi-granularity source-load-storage cooperative dispatch based on combined robust optimization and stochastic optimization for a highway service area micro-energy grid," Renewable Energy, Elsevier, vol. 205(C), pages 747-762.
    16. Alexandre F. M. Correia & Pedro Moura & Aníbal T. de Almeida, 2022. "Technical and Economic Assessment of Battery Storage and Vehicle-to-Grid Systems in Building Microgrids," Energies, MDPI, vol. 15(23), pages 1-23, November.
    17. Andre Leippi & Markus Fleschutz & Michael D. Murphy, 2022. "A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios," Energies, MDPI, vol. 15(9), pages 1-22, April.
    18. Yılmaz, Mehmet & Kaleli, Alirıza & Çorapsız, Muhammed Fatih, 2023. "Machine learning based dynamic super twisting sliding mode controller for increase speed and accuracy of MPPT using real-time data under PSCs," Renewable Energy, Elsevier, vol. 219(P1).
    19. Zeyad A. Almutairi & Ali M. Eltamaly, 2024. "Synergistic Effects of Energy Storage Systems and Demand-Side Management in Optimizing Zero-Carbon Smart Grid Systems," Energies, MDPI, vol. 17(22), pages 1-32, November.
    20. Lam, Dylon Hao Cheng & Lim, Yun Seng & Wong, Jianhui & Allahham, Adib & Patsios, Charalampos, 2023. "A novel characteristic-based degradation model of Li-ion batteries for maximum financial benefits of energy storage system during peak demand reductions," Applied Energy, Elsevier, vol. 343(C).

    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:jeners:v:16:y:2023:i:24:p:8112-:d:1301872. 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.
    Лучший частный хостинг