Review on Advanced Energy Management System for Renewable Based Microgrids Using Optimization and Intelligent Control Techniques
Keywords:
AI-driven EMS, microgrids, renewable energy integration, load balancing, machine learningAbstract
The increasing penetration of renewable energy sources in microgrids introduces challenges related to intermittency, uncertainty, and dynamic load variations. This paper reviews advanced Energy Management System (EMS) strategies that integrate optimization techniques and intelligent control methods for efficient microgrid operation. Classical and metaheuristic optimization approaches are analyzed for energy scheduling, while artificial intelligence-based techniques such as neural networks and reinforcement learning are examined for real-time adaptive control. The study highlights the importance of hybrid EMS frameworks to address multi-objective problems, including cost, reliability, and emission reduction. Key challenges such as scalability, computational complexity, and system security are also discussed. The review concludes that intelligent, optimization-driven EMS solutions are essential for achieving reliable and sustainable operation of renewable-based microgrids.
References
Ozcan, O. F., Kilic, H., & Ozguven, O. F. (2026). Intelligent optimized load shedding under renewable and load uncertainties in fuel cell-integrated islanded microgrids. International Journal of Hydrogen Energy, 214, 153845.
Renjin, Liyunhe, Gongshenggao, & Biantao. (2026). A hybrid fuzzy logic-based energy management strategy for grid-connected photovoltaic microgrids with energy storage optimization. COMPUTERS & ELECTRICAL ENGINEERING, 131.
Jiang, Y. (2026). Enhancing microgrid profitability: ISSA-based optimization of thermal and renewable energy management with CHP considerations. International Journal of Electrical Power & Energy Systems, 174, 111423.
Mahiba, C., Shanmugarathinam, G., Joseph, A., Gayathri, T., & Raja, S. P. (2026). Energy Efficient Federated Edge Reinforcement Optimization for Blockchain-IoT-Enabled Cyber-Physical Control of Renewable Microgrids. Sustainable Computing: Informatics and Systems, 101330.
Irfan, M., Tahir, T., Deilami, S., Huang, S., & Veettil, B. P. (2026). Novel control-based design optimization of smart energy distribution and Management in Vehicle-to-Grid Integrated Microgrid. Applied Energy, 402, 127047.
Gbadega, P. A., Sun, Y., & Balogun, O. A. (2025). Enhanced multi-area automatic generation control in renewable energy-based microgrids using an IPFC-SMES system and COA-optimized FOPID controller. Energy Reports, 13, 6479-6513.
Cheng, Z., Ji, R., Tao, H., Abdalla, A. N., Tang, X., & Li, S. (2026). Robust multi-time-scale scheduling of microgrids with renewable energy interpretation and bidirectionally controlled electric vehicles using adaptive Harris hawks optimization. Unconventional Resources, 100305.
Yang, Y., Xu, J., Ibrahim, A. W., Al-Shamma’a, A. A., Farh, H. M. H., & Hadjaissa, A. (2025). An intelligent control strategy and power management for a microgrid electrical vehicle application based on a hybrid PV/PEMFC/battery renewable energy system. Renewable Energy, 125144.
Manojkumar, R., Reddy, C. K., Yuvaraj, T., Bajaj, M., & Blazek, V. (2025). Optimized rule-based energy management for AC/DC hybrid microgrids using price-based demand response. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 14, 101132.
Limouni, T., Yaagoubi, R., Bouziane, K., Guissi, K., & Baali, E. H. (2025). A comprehensive review of microgrid control methods: Focus on AI, optimization, and predictive techniques. Computers and Electrical Engineering, 125, 110442.
Chothani, N., Upadhyay, P., Patel, D., Chan, C. K., Naik, N., Singh, S., & Dixit, S. (2026). Improving Microgrid Reliability and Performance by Implementing Novel Optimizing Strategies for Renewable Energy and Storage Devices. Energy Nexus, 100671.
El Qouarti, O., Nasser, T., Essadki, A., & Akarne, Y. (2025). AC/DC hybrid microgrid energy management optimization as a decisive factor towards De-Carbonization and rational integration of electrical self-generating units using three-objective grey wolf optimization algorithm-power to X and renewable energies solutions. International Journal of Hydrogen Energy, 138, 1116-1130.
Liu, T., Zou, C., Wang, H., Yang, J., Chi, H., Zhang, H., ... & Xiao, Y. (2026). Intelligent optimization of a PV/T–ORC coupled microgrid: towards reliable, high tenacity and cost-efficient energy systems. Energy Conversion and Management, 347, 120575.
Azakaf, K., El Magri, A., Lajouad, R., & El Myasse, I. (2026). Hybrid energy storage systems in microgrids: A comprehensive review of integration strategies, stability impacts, and optimization approaches. Journal of Energy Storage, 151, 120338.
Güven, A. F. (2025). A novel hybrid Salp Swarm Kepler optimization for optimal sizing and energy management of renewable microgrids with EV integration. Energy, 137696.
Mishra, R. (2024). Raspberry Pi Performance analysis across its Operating System in LED Control Operation. International Journal of Advanced Research and Multidisciplinary Trends (IJARMT), 1(2), 01-11.
Mishra, R. (2025). IOT and DSP (combination of hardcore Virtex-5 FPGA and soft core DSP processor) OFDM System PAPR Reduction Using Artificial Intelligence Algorithm. International Journal of Advanced Research and Multidisciplinary Trends (IJARMT), 2(1), 135-149.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
