Recent research led by Helmy M. El Zoghby from the Department of Electrical Power and Machines Engineering at Helwan University in Cairo, Egypt, presents a promising advancement in the management of islanded microgrids, particularly concerning frequency stability. The study, published in IEEE Access, addresses a critical challenge faced by microgrids powered by renewable energy sources: maintaining consistent electricity frequency amidst the intermittent nature of these resources.
Islanded microgrids are essential for providing sustainable electricity to remote areas, but their reliance on renewable sources can lead to fluctuations in frequency, which can jeopardize system stability. To tackle this issue, El Zoghby and his team propose integrating a green hydrogen energy storage system (GHESS) alongside advanced artificial intelligence (AI) control strategies. The GHESS not only stores excess renewable energy as hydrogen but also converts it back into electricity when needed, thereby reducing dependence on traditional backup generators.
The research highlights the development of a hybrid single-neuron PID (SNPID) controller that utilizes machine learning techniques. In comparative tests against conventional proportional, integral, and derivative (PID) controllers and fuzzy self-tuning PID (FSTPID) controllers, the SNPID controller demonstrated remarkable performance. It achieved a 58% reduction in frequency fluctuations compared to the FSTPID and an impressive 87% reduction compared to the traditional PID controller. El Zoghby states, “The simulation results underscore the SNPID controller’s exceptional performance in frequency stability, emphasizing the transformative potential of AI for microgrid management.”
The commercial implications of this research are significant. As the energy sector increasingly shifts towards renewable sources, the ability to effectively manage and stabilize these systems will be crucial for their widespread adoption. Companies involved in renewable energy generation, energy storage solutions, and smart grid technologies could find substantial opportunities in the integration of GHESS and AI-based controllers. The ability to maintain frequency stability not only enhances the reliability of energy supply but also reduces operational costs associated with backup systems.
This innovative approach positions islanded microgrids as a viable solution for energy access in remote locations, paving the way for sustainable development and energy independence. As the demand for clean energy solutions continues to grow, the findings from this study could play a pivotal role in shaping the future of energy management in microgrid systems.
