Renewable energy sources such as solar and wind power have become key players in the fight against climate change as the world seeks a net-zero future. However, the transition from traditional synchronous generators to renewable energy resources like solar and wind power has led to challenges related to grid stability and reliability.
Xingpeng Li, an assistant professor at the University of Houston, has been awarded a National Science Foundation CAREER Award for his project focusing on addressing the low-inertia challenge in power grids due to the uptake of renewable energy sources. In his project titled Frequency-Constrained Energy Scheduling for Renewable-Dominated Low-Inertia Power Systems, Li aims to ensure the seamless integration of renewable energy sources with the existing power grid without compromising stability.
By leveraging machine learning, Li and his team plan to develop dynamic performance models that can be integrated into the scheduling applications used by grid operators. This approach will not only enhance the efficiency of power systems but also improve grid stability in the face of increased wind and solar generation.
Li is also dedicated to inspiring the next generation of researchers and engineers, with plans to create educational tools and courses to promote the adoption of machine learning in power systems. His work is not only focused on research but also on practical applications and education, ensuring a sustainable and reliable energy future for all.
Li’s research interests extend to energy security, transmission, and renewable power grids both onshore and offshore, contributing to the advancement of net-zero energy systems. With a strong track record of publications and awards, Li is a leading figure in the field of renewable energy integration and grid stability.
