DEEP REINFORCEMENT LEARNING FOR DYNAMIC VOLTAGE STABILITY AND FREQUENCY REGULATION IN MICROGRIDS WITH HIGH RENEWABLE ENERGY PENETRATION
Indonesia 
Abstract
The rapid integration of renewable energy into microgrids introduces complex challenges for maintaining dynamic voltage stability and frequency regulation due to the stochastic and intermittent nature of solar and wind generation. Traditional control methods, including PID and model predictive controllers, often fail to adapt effectively to rapid fluctuations and nonlinear system dynamics, highlighting the need for intelligent, adaptive control strategies.This study aims to investigate the effectiveness of deep reinforcement learning (DRL) for real-time voltage stability and frequency regulation in microgrids with high renewable energy penetration. The research seeks to evaluate DRL’s ability to optimize control actions, improve system resilience, and enhance renewable energy utilization compared to conventional methods. A simulation-based approach was employed, modeling microgrid dynamics with integrated solar and wind sources, energy storage systems, and variable loads. DRL controllers were developed using actor-critic architectures and trained to learn optimal control policies through iterative interaction with the simulated environment. System performance was assessed using voltage deviation, frequency deviation, control effort, renewable utilization, and resilience metrics. DRL-based control significantly reduced voltage and frequency deviations to 0.022 p.u. and 0.037 Hz, respectively, while minimizing control effort to 37% and increasing renewable utilization to 92%. System resilience improved to 0.91, outperforming conventional PID and MPC strategies under varying load and generation scenarios. Deep reinforcement learning provides a robust, adaptive approach for microgrid stability management, enabling enhanced reliability, efficiency, and sustainable integration of high-penetration renewable energy. The study demonstrates DRL’s potential for scalable deployment in complex renewable-rich microgrids.
Full text article
References
Addai, M., & Musilek, P. (2026). Artificial Intelligence-Enhanced Droop Control for Renewable Energy-Based Microgrids: A Comprehensive Review. Electronics, 15(3), 707. https://doi.org/10.3390/electronics15030707
Aghdam, P. A., Khoshkhoo, H., & Akbari, A. (2025). An Intelligent Reinforcement?Learning?Based Load Shedding to Prevent Voltage Instability. In A. Parizad, H. R. Baghaee, & S. Rahman (Eds.), Smart Cyber?Physical Power Systems (1st ed., pp. 449–470). Wiley. https://doi.org/10.1002/9781394334599.ch16
Ahmadi, M., & Aly, H. (2025). A Decentralized Multi-Agent Reinforcement Learning for Fault Detection and Isolation in Distribution Networks. 2025 IEEE Electrical Power and Energy Conference (EPEC), 23–27. https://doi.org/10.1109/EPEC65543.2025.11230425
De Silva, W. M. P., Krishnan, T. N. H., Ho, P. W. C., & Sarimuthu, C. R. (2025). A Novel Meta?Learning?Based Reinforcement Controller for Voltage Regulation of an Interleaved Boost Converter in Solar Photovoltaic Systems. IET Renewable Power Generation, 19(1), e70156. https://doi.org/10.1049/rpg2.70156
Hu, H., Liu, F., Liu, W., Li, H., & Zhu, Q. (2026). A novel integrated strategy for coordinated optimization of energy management and air supply control based on multi-agent reinforcement learning for fuel cell hybrid electric vehicle. Applied Energy, 411, 127571. https://doi.org/10.1016/j.apenergy.2026.127571
Huang, W., Luo, Z., Wen, L., Liu, H., & Wu, J. (2025). Active Power Quality Multi-Objective Coordinated Control Strategy for Distribution Networks Based on Deep Reinforcement Learning. 2025 12th International Forum on Electrical Engineering and Automation (IFEEA), 635–640. https://doi.org/10.1109/IFEEA66847.2025.11388139
Ioannou, I., Javaid, S., Tan, Y., & Vassiliou, V. (2025). A Hierarchical Predictive-Adaptive Control Framework for State-of-Charge Balancing in Mini-Grids Using Deep Reinforcement Learning. Electronics, 15(1), 61. https://doi.org/10.3390/electronics15010061
Jadery, A., Ehsanian, M., Harb, A., & Rachid, E. (2025). A Self-Healing Dual-Frequency PLL using On-Chip Adaptive Phase Noise Compensation via Reinforcement Learning. 2025 IEEE Conference on Antenna Measurements and Applications (CAMA), 1–5. https://doi.org/10.1109/CAMA65664.2025.11335179
Kiasari, M., & Aly, H. (2025). AI-Driven Control Strategies for FACTS Devices in Power Quality Management: A Comprehensive Review. Applied Sciences, 15(22), 12050. https://doi.org/10.3390/app152212050
Kumar, P., Dhundhara, S., Pawar, R., & Arya, Y. (2026). An OA-optimized multi-stage fuzzy PI–PDF control scheme for robust frequency regulation in islanded microgrid. Chaos, Solitons & Fractals, 209, 118395. https://doi.org/10.1016/j.chaos.2026.118395
Liu, A., Hao, S., Li, Y., Miao, Y., & Zuo, H. (2025). Analysis of Operating Characteristics of Flexible Interconnected Devices in New Energy Access Scenarios. 2025 4th International Conference on Clean Energy Storage and Power Engineering (CESPE), 144–149. https://doi.org/10.1109/CESPE68033.2025.00034
Liu, J., Huang, X., Xiong, J., Wang, Y., Liang, X., & Tian, M. (2025). A Deep Reinforcement Learning for Active Voltage Control with Integrated Short-term Voltage Stability Assessment Model. 2025 IEEE 9th Conference on Energy Internet and Energy System Integration (EI2), 900–906. https://doi.org/10.1109/EI268505.2025.11425143
Pan, Y., Wang, M., Gan, H., Jiao, X., Dai, K., Xu, X., Chen, Y., & Chen, Z. (2026). A Graph Reinforcement Learning-Based Charging Guidance Strategy for Electric Vehicles in Faulty Electricity–Transportation Coupled Networks. Symmetry, 18(4), 591. https://doi.org/10.3390/sym18040591
Prasad, S., & Singh, Y. (2026). Coordinated Frequency Control in Microgrids with High Renewable and EV Penetration. IEEE Transactions on Industry Applications, 1–10. https://doi.org/10.1109/TIA.2026.3676973
Qi, X., Liu, J., Zhang, W., & Deng, H. (2026). Distributed Optimal Load Frequency Control in Multi-microgrids Under Transmission Losses and Operational Constraints. IEEE Transactions on Industrial Electronics, 73(4), 5651–5662. https://doi.org/10.1109/TIE.2025.3634411
Ramul, A. R., Shahraki, A. S., Bachache, N. K., & Sadeghi, R. (2025). Cyberspace enhancement of electric vehicle charging stations in smart grids based on detection and resilience measures against hybrid cyberattacks: A multi-agent deep reinforcement learning approach. Energy, 325, 136038. https://doi.org/10.1016/j.energy.2025.136038
Shil, M., Pillai, G. N., & Gupta, M. K. (2025). Cooperative Multi-Agent Deep Reinforcement Learning for Secondary Voltage Control of AC Microgrids for Variable Voltage Sources. 2025 IEEE 5th International Conference on Sustainable Energy and Future Electric Transportation (SEFET), 1–6. https://doi.org/10.1109/SEFET65155.2025.11255080
Subrahmanyam, B., & Hariharan, R. (2025). A Dynamic Weighted Hypergraph Based Deep Learning for Active and Reactive Power control in Three-Phase Grid-Connected Electric Vehicles. 2025 IEEE 6th India Council International Subsections Conference (INDISCON), 1–7. https://doi.org/10.1109/INDISCON66021.2025.11254385
Swami, A. K., Verma, D., Soni, R., & Goswami, D. (2025). Artificial intelligence technology in materials selection, device engineering and parameter optimisation for triboelectric nanogenerator. Materials Today Communications, 46, 112553. https://doi.org/10.1016/j.mtcomm.2025.112553
Wenqiang, X., Hui, C., Yubo, Y., Chenyu, Z., Xiaolong, X., Weiping, Z., & Ning, G. (2026). Adaptive Multi?Objective Coordinated Scheduling of Distribution Areas Based on Deep Reinforcement Learning. Engineering Reports, 8(2), e70624. https://doi.org/10.1002/eng2.70624
Yakine Kouba, N. E., Brik, A., & Selkim, D. (2026). Developing of optimal multi-stage power management and frequency control to coordinate EMS and DMS operations in multi microgrid-nanogrid system. Renewable Energy Focus, 58, 100855. https://doi.org/10.1016/j.ref.2026.100855
Authors
Copyright (c) 2026 Erpan Sahiri
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
