Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning
•A novel coordinated automatic generation control algorithm framework is proposed.•A deep reinforcement learning algorithm is introduced to obtains better performance.•Controllers can realize the coordinated control of multiple areas.•The proposed method can maximize control performance and minimize...
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| Veröffentlicht in: | Applied energy Jg. 306; S. 117900 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
15.01.2022
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| ISSN: | 0306-2619, 1872-9118 |
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| Abstract | •A novel coordinated automatic generation control algorithm framework is proposed.•A deep reinforcement learning algorithm is introduced to obtains better performance.•Controllers can realize the coordinated control of multiple areas.•The proposed method can maximize control performance and minimize payment.
To dynamically balance multiple energy fluctuations in a multi-area integrated energy system (IES), a coordinated power control framework, named distributed intelligent coordinated automatic generation control (DIC-AGC), is constructed among different areas during load frequency control (LFC). Furthermore, an evolutionary imitation curriculum multi-agent deep deterministic policy gradient (EIC-MADDPG) algorithm is proposed as a novel deep reinforcement learning algorithm to realize coordinated control and improve the performance of DIC-AGC in the performance-based frequency regulation market. EIC-MADDPG, which combines imitation learning and curriculum learning, can adaptively derive the optimal coordinated control strategies for multiple areas of LFC controllers through centralized learning and decentralized implementation. The simulation of a four-area LFC-IES model on the China Southern Grid (CSG) demonstrates the effectiveness of the proposed method in maximizing control performance while minimizing regulation mileage payment in every area against stochastic load and renewable power fluctuations. |
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| AbstractList | •A novel coordinated automatic generation control algorithm framework is proposed.•A deep reinforcement learning algorithm is introduced to obtains better performance.•Controllers can realize the coordinated control of multiple areas.•The proposed method can maximize control performance and minimize payment.
To dynamically balance multiple energy fluctuations in a multi-area integrated energy system (IES), a coordinated power control framework, named distributed intelligent coordinated automatic generation control (DIC-AGC), is constructed among different areas during load frequency control (LFC). Furthermore, an evolutionary imitation curriculum multi-agent deep deterministic policy gradient (EIC-MADDPG) algorithm is proposed as a novel deep reinforcement learning algorithm to realize coordinated control and improve the performance of DIC-AGC in the performance-based frequency regulation market. EIC-MADDPG, which combines imitation learning and curriculum learning, can adaptively derive the optimal coordinated control strategies for multiple areas of LFC controllers through centralized learning and decentralized implementation. The simulation of a four-area LFC-IES model on the China Southern Grid (CSG) demonstrates the effectiveness of the proposed method in maximizing control performance while minimizing regulation mileage payment in every area against stochastic load and renewable power fluctuations. To dynamically balance multiple energy fluctuations in a multi-area integrated energy system (IES), a coordinated power control framework, named distributed intelligent coordinated automatic generation control (DIC-AGC), is constructed among different areas during load frequency control (LFC). Furthermore, an evolutionary imitation curriculum multi-agent deep deterministic policy gradient (EIC-MADDPG) algorithm is proposed as a novel deep reinforcement learning algorithm to realize coordinated control and improve the performance of DIC-AGC in the performance-based frequency regulation market. EIC-MADDPG, which combines imitation learning and curriculum learning, can adaptively derive the optimal coordinated control strategies for multiple areas of LFC controllers through centralized learning and decentralized implementation. The simulation of a four-area LFC-IES model on the China Southern Grid (CSG) demonstrates the effectiveness of the proposed method in maximizing control performance while minimizing regulation mileage payment in every area against stochastic load and renewable power fluctuations. |
| ArticleNumber | 117900 |
| Author | Li, Jiawen Zhang, Xiaoshun Yu, Tao |
| Author_xml | – sequence: 1 givenname: Jiawen surname: Li fullname: Li, Jiawen organization: College of Electric Power, South China University of Technology, 510640 Guangzhou, China – sequence: 2 givenname: Tao surname: Yu fullname: Yu, Tao email: taoyu1@scut.edu.cn organization: College of Electric Power, South China University of Technology, 510640 Guangzhou, China – sequence: 3 givenname: Xiaoshun surname: Zhang fullname: Zhang, Xiaoshun organization: College of Engineering, Shantou University, 515063 Shantou, China |
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| Keywords | Performance-based frequency regulation market Evolutionary imitation curriculum multi-agent double delayed deep deterministic policy gradient algorithm (EIC-MADDPG) Distributed intelligent coordinated automatic generation control (DIC-AGC) |
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| SubjectTerms | algorithms China curriculum Distributed intelligent coordinated automatic generation control (DIC-AGC) energy Evolutionary imitation curriculum multi-agent double delayed deep deterministic policy gradient algorithm (EIC-MADDPG) issues and policy markets Performance-based frequency regulation market |
| Title | Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning |
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