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Volume 13
Issue 11
10.3390/pr13113396
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Intelligent Frequency Control for Hybrid Multi-Source Power Systems: A Stepwise Expert-Teaching PPO Approach

by
Jianhong Jiang
1,
Shishu Zhang
1,
Jie Wang
2,
Wenting Shen
1,
Changkui Xue
1,
Qiang Ye
1,
Zhaoyang Lv
1,
Minxing Xu
2 and
Shihong Miao
2,*
1
POWERCHINA Chengdu Engineering Corporation Limited, Chengdu 610072, China
2
State Key Laboratory of Advanced Electromagnetic Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Processes 2025, 13(11), 3396; https://doi.org/10.3390/pr13113396
Submission received: 2 September 2025 / Revised: 8 October 2025 / Accepted: 20 October 2025 / Published: 23 October 2025
(This article belongs to the Special Issue Energy Storage Planning, Control, and Dispatch for Grid Dynamic Enhancement)
Versions Notes

Abstract

This paper proposes a stepwise expert-teaching reinforcement learning framework for intelligent frequency control in hydro–thermal–wind–solar–compressed air energy storage (CAES) integrated systems under high renewable energy penetration. The proposed method addresses the frequency stability challenge in low-inertia, high-volatility power systems, particularly in Southwest China, where large-scale renewable-energy-based energy bases are rapidly emerging. A load frequency control (LFC) model is constructed to serve as the training and validation environment, reflecting the dynamic characteristics of the hybrid system. The stepwise expert-teaching PPO (SETP) framework introduces a stepwise training mechanism in which expert knowledge is embedded to guide the policy learning process and training parameters are dynamically adjusted based on observed performance. Comparative simulations under multiple disturbance scenarios are conducted on benchmark systems. Results show that the proposed method outperforms standard proximal policy optimization (PPO) and traditional PI control in both transient response and coordination performance.
Keywords: frequency regulation; Deep Reinforcement Learning; expert knowledge; multi-source coordination frequency regulation; Deep Reinforcement Learning; expert knowledge; multi-source coordination

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MDPI and ACS Style

Jiang, J.; Zhang, S.; Wang, J.; Shen, W.; Xue, C.; Ye, Q.; Lv, Z.; Xu, M.; Miao, S. Intelligent Frequency Control for Hybrid Multi-Source Power Systems: A Stepwise Expert-Teaching PPO Approach. Processes 2025, 13, 3396. https://doi.org/10.3390/pr13113396

AMA Style

Jiang J, Zhang S, Wang J, Shen W, Xue C, Ye Q, Lv Z, Xu M, Miao S. Intelligent Frequency Control for Hybrid Multi-Source Power Systems: A Stepwise Expert-Teaching PPO Approach. Processes. 2025; 13(11):3396. https://doi.org/10.3390/pr13113396

Chicago/Turabian Style

Jiang, Jianhong, Shishu Zhang, Jie Wang, Wenting Shen, Changkui Xue, Qiang Ye, Zhaoyang Lv, Minxing Xu, and Shihong Miao. 2025. "Intelligent Frequency Control for Hybrid Multi-Source Power Systems: A Stepwise Expert-Teaching PPO Approach" Processes 13, no. 11: 3396. https://doi.org/10.3390/pr13113396

APA Style

Jiang, J., Zhang, S., Wang, J., Shen, W., Xue, C., Ye, Q., Lv, Z., Xu, M., & Miao, S. (2025). Intelligent Frequency Control for Hybrid Multi-Source Power Systems: A Stepwise Expert-Teaching PPO Approach. Processes, 13(11), 3396. https://doi.org/10.3390/pr13113396

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