Renewable energy sources like solar and wind power have emerged as possible heroes in the fight against climate change in the quest to reach a net-zero future based on clean energy. However, the switch from conventional synchronous generators to green energy supplies based on inverters poses a low-inertia challenge to the current power networks, raising issues with stability and reliability.
Improving Power Grid Stability: Innovations in Renewable Energy Integration from the University of Houston
Assistant professor of electrical and computer engineering at the University of Houston, Xingpeng Li, is developing a method that will make it possible for sources of clean energy to be seamlessly integrated with the rest of the power grid without generating any issues. For his project “Frequency-Constrained Energy Scheduling for Renewable-Dominated Low-Inertia Power Systems,” he was granted a CAREER Award by the National Science Foundation. The project aims to guarantee that rapidly increasing wind and solar energy can be supported while maintaining stable and efficient operation of expanding power networks.
The entire kinetic energy stored in the massive, spinning synchronous generators is known as inertia, and it is essential for maintaining system stability, particularly frequency stability, in the event of significant disturbances.
Li clarified, “For instance, the stored kinetic energy instantly converted to electrical energy and addressed the temporary shortfall in generation when the grid experienced sudden large generation losses or increased electrical loads.”
“But since wind and solar have zero marginal costs and produce renewable energy, we want to maximise their use as their share of the grid grows,” he continued.
Improving Grid Functions: Combining AI with Dynamic Performance Models for Effectiveness
Using machine learning, Li and his research team will develop less complex and more effective dynamic performance models. These models will then be integrated into the day-ahead scheduling tool, which grid operators use to schedule all of the generating resources for the upcoming operational day.
“We can more effectively manage with the suggested new modelling and computational methodologies, grid and make sure it can consistently provide all customers with high-quality power.”
Motivating Next Generations: Li’s Projects for Power Engineering Students in K–12 and College
Li also aspires to engage and inspire the next generation, particularly K–12 and college students, as well as graduate students.
In order to give benchmarking skills to the broader research community, his team will also develop a free open-source application. It will educate power engineering to K–12 kids and inspire them to think about professions in the power industry in the future via an intuitive graphical user interface.
To aid in the teaching of complex ideas, the researchers are also creating a brand-new course dubbed “Applied Machine Learning in Power Systems,” or simply “AppML.”
Closing the Gap: A New Power Systems Machine Learning Course Meets Workforce Requirements
“As machine learning technologies aid in the improvement of many power system applications, they are becoming increasingly popular in the field of power systems. It is crucial for engineers and students to acquire knowledge and skills in machine learning (ML) as the need for machine learning expertise increases, according to Li. But nationally, there aren’t many courses that mix machine learning with power systems. This new training will help meet the requirement for workers.
Li was chosen earlier this year as an Early-Career Research Fellow in the Offshore Energy Safety Track by the Gulf Research Programme of the National Academies of Sciences, Engineering, and Medicine.
Fellows are able to work on research projects that they might not otherwise be able to because the early-career funding is not project-specific.
Dr. Li’s Area of Interest: University of Houston Renewable Power Grids and Energy Systems Advancement
Planning and management of diverse electric energy systems, such as microgrids and bulk power grids, are among Li’s research interests. He is in charge of UH’s Renewable Power Grid (RPG) Lab. In addition, he holds the position of Associate Director of the UH Power Electronics, Microgrids & Subsea Electrical Systems Centre (PEMSEC).
Energy security, transmission, and transition for onshore and offshore energy systems are the main areas of concentration for his lab. The group suggests new models and algorithms to increase the security and efficiency of energy systems in an effort to support future power grids that are enhanced by renewable energy sources. By making it easier to integrate green hydrogen and renewable energy sources into the grid, the lab’s work also advances net-zero energy systems.
Research and Industry Accomplishments: Li’s Contributions and Acknowledgments.
Li has published more than 60 peer-reviewed articles, and in recent years, he has been conducting more and more research. The U.S. Department of Energy’s Office of Electricity awarded him Tier-1 and Tier-2 Silver prizes in 2019 for his two winning applications to the Electricity Industry Technology and Practices Innovation Challenge prize programme. In 2023, the Offshore Technology Conference designated him an Emerging Leader, and the Georgia Tech Energy Faculty Fellows programme chose him.
He worked as a senior application engineer for ABB’s Power Grid division, which is now Hitachi Energy, before coming to UH. Additionally, he collaborated with PJM Interconnection and ISO New England’s R&D departments.
Li holds two degrees in electrical engineering from Chinese universities: a bachelor’s from Shandong University with a focus on power engineering and a master’s from Zhejiang University with a focus on power systems. He graduated with a Ph.D. in electrical engineering from Arizona State University, a second master’s degree in industrial engineering from Arizona State University, and a third master’s degree in computer science (machine learning) from Georgia Institute of Technology.
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