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Applied Sciences
Volume 15
Issue 23
10.3390/app152312408
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Single-Step Allowable Action Threshold Determination of Renewable Energy Automatic Generation Control Using Model-Based and Data-Driven Method

by
Ziqi Wang
1,
Gaichao Xue
1,
Yanlou Song
2,
Renkai Liu
3,
Guanghui Chang
1,
Po Wu
2 and
Kaifeng Zhang
3,*
1
State Grid Henan Electric Power Company, Zhengzhou 450003, China
2
State Grid Henan Electric Research Institute, Zhengzhou 450052, China
3
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(23), 12408; https://doi.org/10.3390/app152312408 (registering DOI)
Submission received: 28 October 2025 / Revised: 15 November 2025 / Accepted: 21 November 2025 / Published: 22 November 2025
(This article belongs to the Special Issue Artificial Intelligence (AI) for Energy Systems)
Versions Notes

Abstract

Renewable energy automatic generation control (AGC) has the characteristics of rapid adjustment and flexibility, which play a critical role in frequency regulation. Abnormal outputs in renewable energy AGC may trigger frequency fluctuations and threaten grid security. To address the above problems in renewable energy, AGC, a combined model-based and data-driven method for determining the single-step allowable action threshold, is proposed. Firstly, an AGC model with multiple frequency-regulating units is built, and the threshold can be obtained through simulation considering system sta-tus parameters. Secondly, as the model-based method struggles to satisfy the require-ment of rapidity, a data-driven model based on CNN-LSTM is employed to determine the threshold in real-time. The training data is provided by a model-based method. Considering the limited coverage and interpretability of neural networks, a statistical error-prevention method is proposed to avoid deviations. Then, an adaptive piecewise constant approximation algorithm is employezd to reduce threshold update frequency and the burden for dispatchers. Finally, an adaptive threshold adjustment method for extreme scenarios is proposed, ensuring the frequency regulation of renewable energy AGC under extreme scenarios. Through experiments, the reliability and validity of the proposed method in threshold determination and error prevention are validated.
Keywords: single-step allowable action threshold; renewable energy automatic generation control; model-based method; data-driven method; convolutional neural network; long short-term memory single-step allowable action threshold; renewable energy automatic generation control; model-based method; data-driven method; convolutional neural network; long short-term memory

Share and Cite

MDPI and ACS Style

Wang, Z.; Xue, G.; Song, Y.; Liu, R.; Chang, G.; Wu, P.; Zhang, K. Single-Step Allowable Action Threshold Determination of Renewable Energy Automatic Generation Control Using Model-Based and Data-Driven Method. Appl. Sci. 2025, 15, 12408. https://doi.org/10.3390/app152312408

AMA Style

Wang Z, Xue G, Song Y, Liu R, Chang G, Wu P, Zhang K. Single-Step Allowable Action Threshold Determination of Renewable Energy Automatic Generation Control Using Model-Based and Data-Driven Method. Applied Sciences. 2025; 15(23):12408. https://doi.org/10.3390/app152312408

Chicago/Turabian Style

Wang, Ziqi, Gaichao Xue, Yanlou Song, Renkai Liu, Guanghui Chang, Po Wu, and Kaifeng Zhang. 2025. "Single-Step Allowable Action Threshold Determination of Renewable Energy Automatic Generation Control Using Model-Based and Data-Driven Method" Applied Sciences 15, no. 23: 12408. https://doi.org/10.3390/app152312408

APA Style

Wang, Z., Xue, G., Song, Y., Liu, R., Chang, G., Wu, P., & Zhang, K. (2025). Single-Step Allowable Action Threshold Determination of Renewable Energy Automatic Generation Control Using Model-Based and Data-Driven Method. Applied Sciences, 15(23), 12408. https://doi.org/10.3390/app152312408

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