Breakthrough in Renewable Energy: Researchers Develop Advanced Control Strategy for Microgrids

Researchers at Arizona State University have made a significant contribution to the field of renewable energy by developing a new control strategy for microgrids that leverages machine learning-based robust model predictive control. According to the study published in Results in Engineering, the new strategy is designed to manage the variability of renewable energy sources, such as photovoltaics, wind turbines, and fuel cells, to ensure efficient and reliable power supply. The proposed method has been shown to outperform conventional control methods in simulations conducted under various scenarios.

Key Takeaways:

• The researchers developed a type-2 neural fuzzy robust model predictive control (MPC) strategy for isolated microgrid systems that include various types of renewable energy sources (RES) such as photovoltaics (PVs), wind turbines (WTs), fuel cells (FCs) and battery energy storage systems (BESSs).
• The proposed method uses a restricted Boltzmann machine (RBM) and contrastive divergence (CD) algorithm to adjust settings and optimize MPC control.
• Simulations conducted in MATLAB/Simulink under three different scenarios (normal, 30% uncertainty, and 50% uncertainty) demonstrated the effectiveness of the proposed controller, achieving frequency deviation errors of 0.02 pu., 0.03 pu., and 0.05 pu., respectively.
• The proposed method outperformed type-1 fuzzy MPC and conventional droop controller methods in terms of performance and speed.
• The researchers concluded that the proposed controller has better and faster performance than other methods, making it a potential solution for managing isolated microgrid systems.

Statistics:

• 0.02 pu. frequency deviation error achieved under normal conditions
• 0.03 pu. frequency deviation error achieved with 30% uncertainty
• 0.05 pu. frequency deviation error achieved with 50% uncertainty
• 27():106170 is the article number assigned to the research article in Results in Engineering
• 2025 is the year of publication of the research article
• The research was conducted by a team of authors from Arizona State University, including Chou-Yi Hsu, Amit Ved, Hannah Jessie Rani R, Zayd Ajsan Balsem, Nora Rashid Najem, Abhayveer Singh, P.Sasi Kiran, Ankita Aggarwal, Satish Kumar Samal, and Alireza Kamranfar

Sources:

• Power management in isolated microgrids using machine learning-based robust model predictive control. Results in Engineering, 2025,27():106170.
• VerticalNews. Researchers detail new data in renewable energy. Ecology, Environment & Conservation, September 12, 2025; p 168.