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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| Vydáno v: | Applied energy Ročník 306; s. 117900 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
15.01.2022
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| Témata: | |
| ISSN: | 0306-2619, 1872-9118 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •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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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0306-2619 1872-9118 |
| DOI: | 10.1016/j.apenergy.2021.117900 |