Cooperative Control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm
In power systems, frequency control is essential to ensure stable grid operation. With the increasing share of renewable energy, frequency fluctuations have become more complex and unpredictable. To address this challenge, the paper proposes a cooperative control method for power grid frequency base...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10906594/ |
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| author | Tao Shen Jing Zhang Yu He Shengsun Yang Demu Zhang Zhaorui Yang |
| author_facet | Tao Shen Jing Zhang Yu He Shengsun Yang Demu Zhang Zhaorui Yang |
| author_sort | Tao Shen |
| collection | DOAJ |
| description | In power systems, frequency control is essential to ensure stable grid operation. With the increasing share of renewable energy, frequency fluctuations have become more complex and unpredictable. To address this challenge, the paper proposes a cooperative control method for power grid frequency based on the Expert-Guided Deep Deterministic Policy Gradient (EGDDPG) algorithm. This method is data-driven and continuously interacts with the environment to adaptively optimize the control strategy. On the one hand, EGDDPG incorporates expert data during training to guide the agent, enabling faster learning of effective strategies and reducing training convergence time. On the other hand, random disturbances are introduced to simulate the uncertainties in the power system, encouraging the agent to learn control strategies across different environmental states. This approach avoids the overfitting issues associated with fixed disturbance training, enhancing the adaptability and robustness of the algorithm. Simulation results show that the proposed EGDDPG algorithm converges faster and demonstrates stronger control capability and adaptability across various scenarios. It effectively reduces frequency deviation fluctuations and overshoot. |
| format | Article |
| id | doaj-art-1dee925f347a4a3684535ed4331add99 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1dee925f347a4a3684535ed4331add992025-08-20T02:58:07ZengIEEEIEEE Access2169-35362025-01-0113385023851410.1109/ACCESS.2025.354649210906594Cooperative Control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient AlgorithmTao Shen0https://orcid.org/0009-0009-3800-6241Jing Zhang1https://orcid.org/0000-0002-3732-7432Yu He2https://orcid.org/0000-0002-3299-6426Shengsun Yang3Demu Zhang4Zhaorui Yang5School of Electrical Engineering, Guizhou University, Guiyang, ChinaSchool of Electrical Engineering, Guizhou University, Guiyang, ChinaSchool of Electrical Engineering, Guizhou University, Guiyang, ChinaSchool of Electrical Engineering, Guizhou University, Guiyang, ChinaSchool of Electrical Engineering, Guizhou University, Guiyang, ChinaSchool of Electrical Engineering, Guizhou University, Guiyang, ChinaIn power systems, frequency control is essential to ensure stable grid operation. With the increasing share of renewable energy, frequency fluctuations have become more complex and unpredictable. To address this challenge, the paper proposes a cooperative control method for power grid frequency based on the Expert-Guided Deep Deterministic Policy Gradient (EGDDPG) algorithm. This method is data-driven and continuously interacts with the environment to adaptively optimize the control strategy. On the one hand, EGDDPG incorporates expert data during training to guide the agent, enabling faster learning of effective strategies and reducing training convergence time. On the other hand, random disturbances are introduced to simulate the uncertainties in the power system, encouraging the agent to learn control strategies across different environmental states. This approach avoids the overfitting issues associated with fixed disturbance training, enhancing the adaptability and robustness of the algorithm. Simulation results show that the proposed EGDDPG algorithm converges faster and demonstrates stronger control capability and adaptability across various scenarios. It effectively reduces frequency deviation fluctuations and overshoot.https://ieeexplore.ieee.org/document/10906594/Frequency controlcooperative controlexpert-guideddeep deterministic policy gradient |
| spellingShingle | Tao Shen Jing Zhang Yu He Shengsun Yang Demu Zhang Zhaorui Yang Cooperative Control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm IEEE Access Frequency control cooperative control expert-guided deep deterministic policy gradient |
| title | Cooperative Control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm |
| title_full | Cooperative Control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm |
| title_fullStr | Cooperative Control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm |
| title_full_unstemmed | Cooperative Control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm |
| title_short | Cooperative Control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm |
| title_sort | cooperative control of power grid frequency based on expert guided deep deterministic policy gradient algorithm |
| topic | Frequency control cooperative control expert-guided deep deterministic policy gradient |
| url | https://ieeexplore.ieee.org/document/10906594/ |
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