Dynamics-Constrained Unit Commitment for Low-Inertia Renewable Generation-Dominated Power Grid
To ensure power system operate in a reliable and efficient manner, day-ahead scheduling is performed daily by solving a security-constrained unit commitment (SCUC) problem. Maintaining system frequency within acceptable limits is critical for power system stability. However, with increasing penetration of variable renewable energy in the power system, the number of conventional synchronous generators committed on will be much less than before. This results in a significant reduction in system synchronous inertia, which would negatively affect system frequency stability. This makes it necessary to ensure inertia requirements for future renewable energy-dominated low-inertia power grids. To address this issue, this talk will first discuss a physical model-based inertia-constrained SCUC model and then explain a learning-assisted SCUC model; both models account for G-1 contingency stability. Simulation results show learning-assisted model provides better solution quality at the cost of a long computing time. Lastly, this talk will present several strategies to improve the computational efficiency.
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- Date: 26 Sep 2023
- Time: 05:00 PM UTC to 06:00 PM UTC
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- Starts 15 September 2023 06:14 PM UTC
- Ends 26 September 2023 06:00 PM UTC
- No Admission Charge
Speakers
Dr. Xingpeng Li
Dynamics-Constrained Unit Commitment for Low-Inertia Renewable Generation-Dominated Power Grid
Abstract: To ensure power system operate in a reliable and efficient manner, day-ahead scheduling is performed daily by solving a security-constrained unit commitment (SCUC) problem. Maintaining system frequency within acceptable limits is critical for power system stability. However, with increasing penetration of variable renewable energy in the power system, the number of conventional synchronous generators committed on will be much less than before. This results in a significant reduction in system synchronous inertia, which would negatively affect system frequency stability. This makes it necessary to ensure inertia requirements for future renewable energy-dominated low-inertia power grids. To address this issue, this talk will first discuss a physical model-based inertia-constrained SCUC model and then explain a learning-assisted SCUC model; both models account for G-1 contingency stability. Simulation results show learning-assisted model provides better solution quality at the cost of a long computing time. Last, this talk will present several strategies to improve the computational efficiency.
Biography:
Dr. Xingpeng Li is currently an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Houston. He previously worked for ISO New England and PJM Interconnection. Before joining the University of Houston in 2018, he was a senior engineer with ABB’s Power Grid division that is now Hitachi Energy. He received the Ph.D. in electrical engineering from Arizona State University in 2017. His research interests include power system operation, control and planning, grid integration of renewable energy and energy storage, and microgrid sizing and energy management.