Parth Patel

DOI: https://doi.org/10.5281/zenodo.23453918

The accelerated worldwide deployment of electric vehicles is profoundly reshaping electrical distribution systems, introducing significant operational constraints regarding network stability while simultaneously unlocking novel paradigms for distributed energy resource management. Uncoordinated Level 3 direct current fast charging (DCFC) and megawatt-level fleet hubs introduces localized transformer thermal overloading, voltage unbalance, and severe harmonic distortion. To mitigate these impacts, multi-energy microgrids (MEMGs) integrating solar photovoltaics (PV), wind generators, battery energy storage systems (BESS), and green hydrogen vectors (electrolyzers and fuel cells) are emerging as essential infrastructure. This paper presents a systematic review of multi-energy EV charging microgrids following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, synthesizing literature published between 2018 and 2026. A comprehensive taxonomy has been established across three primary domains: (1) power electronic converter topologies and hybrid AC/DC microgrid architectures, evaluating control strategies for constant power load (CPL) stability; (2) Vehicle-to-Everything (V2X) flexibility services, analysing aggregator market mechanics and Distribution Locational Marginal Pricing (DLMP); and (3) green hydrogen sector coupling, evaluating co-located Fuel Cell Electric Vehicle (FCEV) refuelling hubs and seasonal storage dynamics. Furthermore, this review examines non-linear electro-thermal battery degradation modelling within multi-objective energy management systems (EMS) and metaheuristic optimization algorithms (e.g., NSGA-III, MOROA, HOMER). Finally, critical research gaps are identified in low-inertia Virtual Synchronous Generator (VSG) control, cybersecurity against False Data Injection (FDI) attacks, and Inter-industry regulatory alignment.

Electric vehicle charging; multi-energy microgrid; Vehicle-to-Grid (V2G); Green hydrogen; Battery degradation; Power electronics

This systematic review establishes a comprehensive taxonomy and critical comparative assessment of multi-energy electric vehicle charging microgrids. Through a rigorous synthesis of 1,482 identified records refined under the PRISMA framework, the findings demonstrate that re-architecting EV charging infrastructure from passive demand sinks into dispatchable, multi-energy prosumer nodes constitute an indispensable pathway toward grid-level decarbonization and distribution network resilience. Key insights established in this survey include: 1.Architectural Optimization: Common DC bus and hybrid AC/DC bus microgrid architectures achieve superior round-trip conversion efficiency (88% – 96%) compared to traditional common AC bus networks, provided that CPL negative incremental impedance instabilities are mitigated using FA-PSO-tuned ANFIS and fractional-order (FO-PID) controllers. 2.V2X Flexibility Services: Bidirectional V2G, V2B, and V2H operations provide fast-acting frequency regulation (<100ms) and reactive power compensation. However, commercial viability requires bi-level aggregator market formulations and DLMP dynamic pricing that compensate vehicle owners for cycling-induced battery capacity fade. 3.Hydrogen Sector Coupling: Co-locating BEV DC fast-charging with FCEV hydrogen refuelling hubs creates a symbiotic multi-energy ecosystem where water electrolyzers absorb excess renewable power, hydrogen tanks provide seasonal zero-self-discharge storage, and fuel cells guarantee multi-day grid resilience. 4.Research Frontiers: Addressing remaining open challenges requires urgent research into Virtual Synchronous Generator (VSG) grid-forming controls for low-inertia microgrids, privacy-preserving federated learning architectures against FDI cyber-attacks, and harmonized cross-sector regulatory tariffs.

1.Acharige, S.S.G., Haque, M.E., et al., 2023. Review of electric vehicle charging technologies, standards, architectures, and converter configurations. IEEE Access 11, 41200-41220. https://doi.org/10.1109/ACCESS.2023.3268900 2.Afshar, S., Macedo, P., Mohamed, F., Disfani, V., 2021. Mobile charging stations for electric vehicles — A review. Renewable and Sustainable Energy Reviews 152, 111654. https://doi.org/10.1016/j.rser.2021.111654 3.Akshay, K., Grace, G.H., Gunasekaran, K., Samikannu, R., 2024. Power consumption prediction for electric vehicle charging stations and forecasting income. Scientific Reports 14, 6497. https://doi.org/10.1038/s41598-024-56507-2 4.Aljanad, A., Mohamed, A., Shareef, H., Khatib, T., 2018. A novel method for optimal placement of vehicle-to-grid charging stations in distribution power system using a quantum binary lightning search algorithm. Sustainable Cities and Society 38, 174-183. https://doi.org/10.1016/j.scs.2017.12.035 5.Boulakhbar, M., et al., 2024. Deep learning models for predicting electric vehicle charging demand: Comparative analysis and empirical validation. Scientific Reports 14, 56507. https://doi.org/10.1038/s41598-024-58024-8 6.Brady, J., O’Mahony, M., 2016. Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data. Sustainable Cities and Society 26, 203-216. https://doi.org/10.1016/j.scs.2016.06.014 7.Çelik, S., Ok, Ş., 2024. Optimizing electric vehicle charging station placement using p-median model and discrete-event simulation. Heliyon 10, e29153. https://doi.org/10.1016/j.heliyon.2024.e29153 8.Dimitriadou, A., Natsiavas, S., Kouramas, K., 2023. Techno-economic analysis and optimal sizing of a hybrid PV-wind-battery-hydrogen microgrid for electric vehicle charging stations. Journal of Energy Storage 72, 108521. https://doi.org/10.1016/j.est.2023.108521 9.Ghanbari Motlagh, S., Li, L., 2026. Data-driven spatio-temporal EV charging demand forecasting: A comprehensive survey of deep learning and graph neural network architectures. Journal of Energy Storage 141, 119325. https://doi.org/10.1016/j.est.2025.119325 10.Kumar, B.A., Jyothi, B., Singh, A.R., Bajaj, M., Rathore, R.S., Tuka, M.B., 2024. Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience. Scientific Reports 14, 58024. https://doi.org/10.1038/s41598-024-58024-8 11.Li, R., Jenn, A., 2024. Impact of electric vehicle charging demand on power distribution grid congestion and transformer thermal degradation. Nature Communications 15, 3421. https://doi.org/10.1038/s41467-024-55696-8 12.Mastoi, M.S., Zhuang, S., Munir, H.M., et al., 2022. An in-depth exploration of electric vehicle charging station infrastructure: A comprehensive review of challenges, mitigation approaches, and optimization strategies. Energy Reports 8, 11504-11529. https://doi.org/10.1016/j.egyr.2022.06.043 13.Rachid, A., El-Hajj, A., Mansour, C., 2023. Techno-economic and environmental assessment of a multi-energy microgrid powering electric and hydrogen vehicle charging stations. Energy Conversion and Management 291, 117312. https://doi.org/10.1016/j.enconman.2023.117312 14.Rashid, M., Elfouly, T., Chen, N., 2024. A comprehensive survey of electric vehicle charging demand forecasting techniques. IEEE Open Journal of Vehicular Technology 5, 310-335. https://doi.org/10.1109/OJVT.2024.3391000 15.Sagar, A., Kashyap, A., Nasab, M.A., Padmanaban, S., Bertoluzzo, M., Kumar, A., Blaabjerg, F., 2023. A comprehensive review of the recent development of wireless power transfer technologies for electric vehicle charging systems. IEEE Access 11, 81200-81225. https://doi.org/10.1109/ACCESS.2023.3300475 16.Sarda, J., et al., 2024. Integration of electric vehicle charging infrastructure into distribution networks: Standards, grid impacts, and mitigation techniques. Energy Reports 12, 5671-5692. https://doi.org/10.1016/j.egyr.2024.05.021 17.Senapati, M.K., Al Zaabi, O., Al Hosani, K., Al Jaafari, K., Pradhan, C., Muduli, U.R., 2024. Advancing electric vehicle charging ecosystems with intelligent control of DC microgrid stability. IEEE Transactions on Industry Applications 60, 45210-45225. https://doi.org/10.1109/TIA.2024.3382101 18.Singh, A.R., et al., 2024a. A comprehensive review and analysis of the allocation of electric vehicle charging stations in distribution networks. IEEE Access 12, 10210-10235. https://doi.org/10.1109/ACCESS.2024.3361200 19.Singh, A.R., et al., 2024b. Summary of different charging infrastructures and renewable integration. Alexandria Engineering Journal 105, 300-330. https://doi.org/10.1016/j.aej.2024.03.015 20.Tayri, M., Ma, Z., 2025. Smart charging and vehicle-to-grid integration in power distribution networks: Market mechanisms, dynamic pricing, and battery degradation trade-offs. Applied Energy 355, 122210. https://doi.org/10.1016/j.apenergy.2024.122210 21.Triviño-Cabrera, A., Aguado, J.A., de la Torre, S., 2019. Joint routing and scheduling for electric vehicles in smart grids with V2G. Energy 175, 113-122. https://doi.org/10.1016/j.energy.2019.02.184 22.Wang, Z., et al., 2022. Optimal location planning of EV charging stations based on total social cost and genetic algorithm. Energy 240, 122150. https://doi.org/10.1016/j.energy.2021.122150 23.Xydas, E., Marmaras, C., Cipcigan, L.M., Jenkins, N., Carroll, S., Barker, M., 2016. A data-driven approach for characterising the charging demand of electric vehicles: A U.K. case study. Applied Energy 162, 763-771. https://doi.org/10.1016/j.apenergy.2015.10.155 24.Yousuf, A., Wang, Z., Paranjape, R., Tang, Y., 2024. An in-depth exploration of electric vehicle charging station infrastructure: A comprehensive review of challenges, mitigation approaches, and optimization strategies. IEEE Access 12, 51570-51589. https://doi.org/10.1109/ACCESS.2024.3385100 25.Yuvaraj, S., Devabalaji, K.R., Thanikanti, S.B., 2024. Optimal allocation and sizing of multi-energy electric vehicle charging hubs in distribution networks considering vehicle-to-grid flexibility. Sustainable Cities and Society 101, 105120. https://doi.org/10.1016/j.scs.2023.105120 26.Zhang, W., et al., 2025. Impact of high electric vehicle penetration on distribution grids and optimization strategies for grid performance. Energies 18, 03807. https://doi.org/10.3390/en1803807 27.Zhao, Y., et al., 2025. Global EV charging and sustainable energy solutions: International standards, policy frameworks, and hydrogen integration. World Electric Vehicle Journal 16, 00194. https://doi.org/10.3390/wevj16010194 28.Zhu, J., et al., 2023. Agent-based modeling of EV owner charging behavior and transformer life exceedance in residential networks. Renewable and Sustainable Energy Reviews 145, 111120. https://doi.org/10.1016/j.rser.2023.111120

Parth Patel (2026). Multi-Energy Microgrids for Sustainable Electric Vehicle Infrastructure: A Systematic Review of Hybrid Architectures, V2X Flexibility Services, and Hydrogen Sector Coupling. International Journal of Electrical Engineering and Ethics, 9(5), 37–59. ISSN: 2456-9771. DOI: https://doi.org/10.5281/zenodo.23453918