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Smart Decentralized Electric Vehicle Aggregators for Optimal Dispatch Technologies

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  • 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
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    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).
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    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.

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