AI-Driven Adaptive Energy Management and Fault-Resilient Control Framework for Renewable-Integrated Smart Microgrids with EV Charging Infrastructure
Main Article Content
Abstract
The increasing integration of renewable energy sources and electric vehicle (EV) charging infrastructure has improved the sustainability of smart microgrids, but it has also introduced new operational challenges due to renewable power fluctuation, peak EV charging demand, battery constraints, and fault conditions. Conventional energy management methods often fail to coordinate renewable generation, battery storage, grid exchange, and EV charging while maintaining reliable operation during abnormal conditions. To address these limitations, this paper proposes an AI-driven adaptive energy management and fault-resilient control framework for renewable-integrated smart microgrids with EV charging infrastructure. The proposed approach combines AI-assisted short-term forecasting, rule-based battery scheduling, EV charging coordination, and threshold-based fault detection and recovery. A proposed microgrid simulation dataset containing 720 hourly samples over 30 days is used to evaluate solar PV generation, wind generation, load demand, EV charging demand, battery operation, grid exchange, and fault scenarios. The simulation results show that the proposed framework achieves 97.98% renewable energy utilization, 99.08% EV charging satisfaction, and a reduced EV charging delay ratio of 0.92%. It also reduces total energy cost to INR 304,436.02 and lowers grid dependency to 68.36%. Under abnormal conditions, the framework achieves 98.06% fault detection accuracy and 94.80% fault recovery efficiency. These results confirm that the proposed framework provides a simple, adaptive, and reliable solution for improving energy management, EV charging coordination, and fault-resilient operation in renewable-integrated smart microgrids
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJCERT Policy:
The published work presented in this paper is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that the content of this paper can be shared, copied, and redistributed in any medium or format, as long as the original author is properly attributed. Additionally, any derivative works based on this paper must also be licensed under the same terms. This licensing agreement allows for broad dissemination and use of the work while maintaining the author's rights and recognition.
By submitting this paper to IJCERT, the author(s) agree to these licensing terms and confirm that the work is original and does not infringe on any third-party copyright or intellectual property rights.
References
M. R. Khan, Z. M. Haider, F. H. Malik, F. M. Almasoudi, K. S. S. Alatawi, and M. S. Bhutta, “A comprehensive review of microgrid energy management strategies considering electric vehicles, energy storage systems, and AI techniques,” Processes, vol. 12, no. 2, Art. no. 270, 2024, doi: 10.3390/pr12020270.
L. Ahmethodžić, M. Musić, and S. Huseinbegović, “Microgrid energy management: Classification, review and challenges,” CSEE Journal of Power and Energy Systems, vol. 9, no. 4, pp. 1425–1438, 2023, doi: 10.17775/CSEEJPES.2021.09150.
A. Cagnano, E. De Tuglie, and P. Mancarella, “Microgrids: Overview and guidelines for practical implementations and operation,” Applied Energy, vol. 258, Art. no. 114039, 2020, doi: 10.1016/j.apenergy.2019.114039.
S. Fazal, M. E. Haque, M. T. Arif, A. Gargoom, and A. M. T. Oo, “Grid integration impacts and control strategies for renewable based microgrid,” Sustainable Energy Technologies and Assessments, vol. 56, Art. no. 103069, 2023, doi: 10.1016/j.seta.2023.103069.
S. Kirubadevi, T. Sathesh Kumar, S. Sivarajan, and C. Venkata Krishna Reddy, “Optimizing cost and emission reduction in photovoltaic–battery-energy-storage-system-integrated electric vehicle charging stations: An efficient hybrid approach,” Energy Technology, vol. 12, no. 7, Art. no. 2301131, 2024, doi: 10.1002/ente.202301131.
H. S. Das, M. M. Rahman, S. Li, and C. W. Tan, “Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review,” Renewable and Sustainable Energy Reviews, vol. 120, Art. no. 109618, 2020, doi: 10.1016/j.rser.2019.109618.
Y. Wu, Z. Wang, Y. Huangfu, A. Ravey, D. Chrenko, and F. Gao, “Hierarchical operation of electric vehicle charging station in smart grid integration applications—An overview,” International Journal of Electrical Power & Energy Systems, vol. 139, Art. no. 108005, 2022, doi: 10.1016/j.ijepes.2022.108005.
A. Ndiaye, F. Locment, A. De Bernardinis, M. Sechilariu, and E. Redondo-Iglesias, “A techno-economic analysis of energy storage components of microgrids for improving energy management strategies,” Energies, vol. 15, no. 4, Art. no. 1556, 2022, doi: 10.3390/en15041556.
A. Joshi, S. Capezza, A. Alhaji, and M.-Y. Chow, “Survey on AI and machine learning techniques for microgrid energy management systems,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 7, pp. 1513–1529, Jul. 2023, doi: 10.1109/JAS.2023.123657.
P. Sharma, H. D. Mathur, P. Mishra, and R. C. Bansal, “A critical and comparative review of energy management strategies for microgrids,” Applied Energy, vol. 327, Art. no. 120028, 2022, doi: 10.1016/j.apenergy.2022.120028.
O. A. Talab and I. Avci, “Energy management in microgrids using model-free deep reinforcement learning approach,” IEEE Access, vol. 13, pp. 5871–5891, 2025, doi: 10.1109/ACCESS.2025.3525843.
M. W. Altaf, M. T. Arif, S. N. Islam, and M. E. Haque, “Microgrid protection challenges and mitigation approaches—A comprehensive review,” IEEE Access, vol. 10, pp. 38895–38922, 2022, doi: 10.1109/ACCESS.2022.3165011.
M. Barkhi, J. Pourhossein, and S. A. Hosseini, “Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty,” Scientific Reports, vol. 14, Art. no. 28466, 2024, doi: 10.1038/s41598-024-77982-7.
O. Izquierdo-Monge, P. Peña-Carro, A. Hernández-Jiménez, A. Zorita-Lamadrid, and L. Hernández-Callejo, “Methodology for energy management in a smart microgrid based on the efficiency of dispatchable renewable generation sources and distributed storage systems,” Applied Sciences, vol. 14, no. 5, Art. no. 1946, 2024, doi: 10.3390/app14051946.
Y.-T. Jiao, L. Ding, Z.-M. Kong, and C.-Y. Chen, “Distributed observer-based resilient control of islanded DC microgrids under actuator faults,” Transactions of the Institute of Measurement and Control, vol. 46, no. 8, pp. 1519–1534, 2024, doi: 10.1177/01423312231184277.
S. E. Eyimaya and N. Altin, “Review of energy management systems in microgrids,” Applied Sciences, vol. 14, no. 3, Art. no. 1249, 2024, doi: 10.3390/app14031249..
A. Cabrera-Tobar, A. Massi Pavan, G. Petrone, and G. Spagnuolo, “A review of the optimization and control techniques in the presence of uncertainties for the energy management of microgrids,” Energies, vol. 15, no. 23, Art. no. 9114, 2022, doi: 10.3390/en15239114.
V. Suresh, P. Janik, J. M. Guerrero, Z. Leonowicz, and T. Sikorski, “Microgrid energy management system with embedded deep learning forecaster and combined optimizer,” IEEE Access, vol. 8, pp. 202225–202239, 2020, doi: 10.1109/ACCESS.2020.3036131.
B. She, F. Li, H. Cui, J. Zhang, and R. Bo, “Fusion of microgrid control with model-free reinforcement learning: Review and vision,” IEEE Transactions on Smart Grid, vol. 14, no. 4, pp. 3232–3245, Jul. 2023, doi: 10.1109/TSG.2022.3222323.
S. Mahjoub, L. Chrifi-Alaoui, S. Drid, and N. Derbel, “Control and implementation of an energy management strategy for a PV–wind–battery microgrid based on an intelligent prediction algorithm of energy production,” Energies, vol. 16, no. 4, Art. no. 1883, 2023, doi: 10.3390/en16041883.
R. Kandari, N. Neeraj, and A. Micallef, “Review on recent strategies for integrating energy storage systems in microgrids,” Energies, vol. 16, no. 1, Art. no. 317, 2023, doi: 10.3390/en16010317.
S. J. Yaqoob, H. Arnoos, M. A. Qasim, E. B. Agyekum, A. Alzahrani, and S. Kamel, “An optimal energy management strategy for a photovoltaic/li-ion battery power system for DC microgrid application,” Frontiers in Energy Research, vol. 10, Art. no. 1066231, 2023, doi: 10.3389/fenrg.2022.1066231.
S. Iqbal, S. Habib, M. Ali, A. Shafiq, A. ur Rehman, E. M. Ahmed, T. Khurshaid, and S. Kamel, “The impact of V2G charging/discharging strategy on the microgrid environment considering stochastic methods,” Sustainability, vol. 14, no. 20, Art. no. 13211, 2022, doi: 10.3390/su142013211.
A. Karameros, A. Chassiakos, and S. Karatzas, “Design and evaluation of a micro-grid energy management scheme focusing on the integration of electric vehicles,” Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 11, no. 1, Art. no. 1090413, Mar. 2023, doi: 10.13044/j.sdewes.d9.0413.
A. Khazali, Y. Al-Wreikat, E. J. Fraser, M. Naderi, M. J. Smith, S. M. Sharkh, R. G. Wills, D. T. Gladwin, D. A. Stone, and A. J. Cruden, “Sizing a renewable-based microgrid to supply an electric vehicle charging station: A design and modelling approach,” World Electric Vehicle Journal, vol. 15, no. 8, Art. no. 363, 2024, doi: 10.3390/wevj15080363.
S. R. Fahim, S. K. Sarker, S. M. Muyeen, M. R. I. Sheikh, and S. K. Das, “Microgrid fault detection and classification: Machine learning based approach, comparison, and reviews,” Energies, vol. 13, no. 13, Art. no. 3460, 2020, doi: 10.3390/en13133460.
S. Salehimehr, S. M. Miraftabzadeh, and M. Brenna, “A novel machine learning-based approach for fault detection and location in low-voltage DC microgrids,” Sustainability, vol. 16, no. 7, Art. no. 2821, 2024, doi: 10.3390/su16072821.
M. M. Zaben, M. Y. Worku, M. A. Hassan, and M. A. Abido, “Machine learning methods for fault diagnosis in AC microgrids: A systematic review,” IEEE Access, vol. 12, pp. 20260–20298, 2024, doi: 10.1109/ACCESS.2024.3360330.
M. Bilal, A. A. Algethami, Imdadullah, and S. Hameed, “Review of computational intelligence approaches for microgrid energy management,” IEEE Access, vol. 12, pp. 123294–123321, 2024, doi: 10.1109/ACCESS.2024.3440885.
J. Gutiérrez-Escalona, C. Roncero-Clemente, O. Husev, O. Matiushkin, and F. Blaabjerg, “Artificial intelligence in the hierarchical control of AC, DC, and hybrid AC/DC microgrids: A review,” IEEE Access, vol. 12, pp. 157227–157246, 2024, doi: 10.1109/ACCESS.2024.3486382.
S. Jamal, J. Pasupuleti, and J. Ekanayake, “A rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic algorithm optimization,” Scientific Reports, vol. 14, Art. no. 4865, 2024, doi: 10.1038/s41598-024-54333-0.
R. Xu, M. Seatle, C. Kennedy, and M. McPherson, “Flexible electric vehicle charging and its role in variable renewable energy integration,” Environmental Systems Research, vol. 12, Art. no. 11, 2023, doi: 10.1186/s40068-023-00293-9.
J. Tobajas, F. Garcia-Torres, P. Roncero-Sánchez, J. Vázquez, L. Bellatreche, and E. Nieto, “Resilience-oriented schedule of microgrids with hybrid energy storage system using model predictive control,” Applied Energy, vol. 306, Part B, Art. no. 118092, 2022, doi: 10.1016/j.apenergy.2021.118092.