Robust Emergency Frequency Control with Wind Turbines via Decoupling Security Constraints and Modeling Uncertainties

Guanghu Xu, Shuaishuai Feng (), Deping Ke, Huanhuan Yang, Siyang Liao and Jian Xu
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Guanghu Xu: Power Dispatching and Control Center, China Southern Power Grid, Guangzhou 510623, China
Shuaishuai Feng: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Deping Ke: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Huanhuan Yang: Power Dispatching and Control Center, China Southern Power Grid, Guangzhou 510623, China
Siyang Liao: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Jian Xu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Sustainability, 2025, vol. 17, issue 5, 1-18

Abstract: In this paper, controllable wind turbines are employed for emergency frequency control to reduce cost. However, controllable wind turbines introduce significant modeling uncertainties in the form of uncertain environmental factors and model aggregation into the system. To pre-generate feasible emergency control strategies at the lowest cost, a robust optimization model is constructed for a potentially serious fault in power systems, which is a mixed-integer nonlinear robust programming issue with non-analytical calculation (for post-event system frequency). Therefore, this study proposes to first quantify the impact of modeling uncertainties on wind turbine regulation and find the most unfavorable uncertain scenarios for emergency frequency control. Then, based on the above-filtered scenarios, the original problem is transformed into mixed-integer nonlinear programming. A novel simplified algorithm is proposed to solve the above problem efficiently. Finally, a case study is conducted on a real regional power system. It is proved effective in reducing costs by regulating wind turbines for emergency control, and the proposed method is effective in dealing with the impact of modeling uncertainties. Also, high solving efficiency (12 s in case study) meets the demand for efficient online pre-decision-making for emergency control. The research is meaningful in the promotion of secure access to power systems for sustainable power.

Keywords: emergency frequency control; wind turbines; sustainable power; decouple modeling uncertainties; robust control; rapid optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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