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. 2025 May 26;15(1):18379.
doi: 10.1038/s41598-025-03310-2.

Efficient load frequency controller for a power system comprising renewable resources based on deep reinforcement learning

Mohamed A El-Hameed  1 , Mahfouz Saeed  2 , Adnan Kabbani  3 , Enas Abd El-Hay  4
Affiliations

Efficient load frequency controller for a power system comprising renewable resources based on deep reinforcement learning

Mohamed A El-Hameed et al. Sci Rep. .

Abstract

This paper presents the development of an adaptive load frequency controller (LFC) to mitigate frequency deviations in power systems comprising renewable energy sources (RESs) during transient and steady-state conditions. Integrating RESs with power systems results in frequency problems due to reduced system inertia and the intermittency of the RESs. The paper introduces a model-free controller that employs deep neural networks trained by the twin-delayed deep deterministic gradient reinforcement learning policy to generate the load reference signal (LRS) for the speed governor. The LRS is produced by the controller's agent, which undergoes training by receiving observations and rewards from the power system. These observations capture frequency errors resulting from load disturbances and renewable power fluctuations, while the reward assesses the controller's effectiveness in minimizing frequency errors. Compared to heuristic-based controllers, the proposed controller demonstrates considerable improvements in frequency stability for both steady-state error and transient response across various load disturbances when compared to heuristic-based controllers. Moreover, the proposed controller could limit the frequency deviations under varying weather conditions.

Keywords: Deep neural networks; Load frequency control; Reinforcement learning; Renewable resources.

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Conflict of interest statement

Declarations. Competing interest: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Single area power system penetrated with renewable resources.
Fig. 2
Fig. 2
Power curve of GEMSA wind turbine model G52.
Fig. 3
Fig. 3
RL-based controller.
Fig. 4
Fig. 4
Network architecture of the TD3 method.
Fig. 5
Fig. 5
RL-LFC training trend with episodes.
Fig. 6
Fig. 6
Convergence curves of PSO and ILA.
Fig. 7
Fig. 7
Frequency response with RL-LFC and PI controllers with 5% sudden load increase.
Fig. 8
Fig. 8
Frequency response with RL-LFC and PI controllers with 40% sudden load increase.
Fig. 9
Fig. 9
GDB and GRC nonlinearities.
Fig. 10
Fig. 10
Effect of GDB and GRC on frequency response.
Fig. 11
Fig. 11
Variations in wind speed, irradiance, and temperature.
Fig. 12
Fig. 12
Frequency response with varying weather conditions.
See this image and copyright information in PMC

References

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