In today’s rapidly evolving energy landscape, our power grid is experiencing unprecedented changes due to the widespread adoption of renewable energy sources, the proliferation of data centers, and the fast growth of electric vehicles. These advancements, while promoting sustainability and technological progress, introduce significant challenges in maintaining a reliable and efficient electricity system.
Qianxue Xia,
Oak Ridge National Laboratory (ORNL)
This talk explores the design, advanced modeling, and control of a hybrid power plant integrating solar photovoltaic and energy storage systems. We will highlight innovative control strategies, such as machine learning-assisted control and model predictive control, which can improve system efficiency by 22%, and enhance system dynamic performance under grid disturbances. Additionally, we will introduce high-fidelity modeling and simulation techniques capable of achieving up to 10,000 times faster performance. Building on this foundation, we will scale up our analysis to the broader power grid using electromagnetic transient simulations to model large-scale renewable energy integration. This approach enables us to assess the performance of grid-connected power electronics, including solar and wind power, under weak grid conditions, providing a comprehensive understanding of control interactions in renewable energy integration. These advancements are crucial for the future grid’s control and protection design, ensuring a secure and reliable power system in an era of increasing renewable energy penetration.
Dr. Qianxue Xia is an R&D Associate Staff Member in the Grid Research Integration and Development Center at Oak Ridge National Laboratory (ORNL) where she was previously a postdoctoral researcher. In 2022, she earned a Ph.D. in electrical engineering with a minor in computer science, specializing in machine learning, from Georgia Institute of Technology. She also holds a master’s degree in electrical engineering from Arizona State University, obtained in 2018. Dr. Xia’s research focuses on renewable energy integration into the grid, the design, modeling, and control of grid-connected power electronics-based resources, power system electromagnetic transient simulation, and the application of artificial intelligence and machine learning to power system and power electronics.