Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning
In recent years, there has been an increasing need for effective voltage control methods in power systems due to the growing complexity and dynamic nature of practical power grid operations. To enhance the controller’s resilience in addressing communication failures, a dynamic voltage con...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Access Journal of Power and Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10679222/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592850163859456 |
|---|---|
| author | Yuling Wang Vijay Vittal |
| author_facet | Yuling Wang Vijay Vittal |
| author_sort | Yuling Wang |
| collection | DOAJ |
| description | In recent years, there has been an increasing need for effective voltage control methods in power systems due to the growing complexity and dynamic nature of practical power grid operations. To enhance the controller’s resilience in addressing communication failures, a dynamic voltage control method employing distributed execution multi-agent deep reinforcement learning(DRL) is proposed. The proposed method follows a centralized training and decentralized execution based approach. Each agent has independent actor neural networks to output generator control commands and critic neural networks that evaluate command performance. Detailed dynamic models are integrated for agent training to effectively capture the system’s dynamic behavior following disturbances. Subsequent to training, each agent possesses the capability to autonomously generate control commands utilizing only local information. Simulation outcomes underscore the efficacy of the distributed execution multi-agent DRL controller, showcasing its capability in not only providing voltage support but also effectively handling communication failures among agents. |
| format | Article |
| id | doaj-art-3b352ad4f7c24e2a8e25f88f312e3f00 |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-3b352ad4f7c24e2a8e25f88f312e3f002025-01-21T00:03:06ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011150851910.1109/OAJPE.2024.345900210679222Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement LearningYuling Wang0https://orcid.org/0000-0003-1524-5572Vijay Vittal1https://orcid.org/0000-0001-5886-9698School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USASchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USAIn recent years, there has been an increasing need for effective voltage control methods in power systems due to the growing complexity and dynamic nature of practical power grid operations. To enhance the controller’s resilience in addressing communication failures, a dynamic voltage control method employing distributed execution multi-agent deep reinforcement learning(DRL) is proposed. The proposed method follows a centralized training and decentralized execution based approach. Each agent has independent actor neural networks to output generator control commands and critic neural networks that evaluate command performance. Detailed dynamic models are integrated for agent training to effectively capture the system’s dynamic behavior following disturbances. Subsequent to training, each agent possesses the capability to autonomously generate control commands utilizing only local information. Simulation outcomes underscore the efficacy of the distributed execution multi-agent DRL controller, showcasing its capability in not only providing voltage support but also effectively handling communication failures among agents.https://ieeexplore.ieee.org/document/10679222/Voltage controldata-drivendecentralized controlmulti-agentdynamic controlreal-time |
| spellingShingle | Yuling Wang Vijay Vittal Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning IEEE Open Access Journal of Power and Energy Voltage control data-driven decentralized control multi-agent dynamic control real-time |
| title | Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning |
| title_full | Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning |
| title_fullStr | Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning |
| title_full_unstemmed | Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning |
| title_short | Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning |
| title_sort | data driven real time dynamic voltage control using decentralized execution multi agent deep reinforcement learning |
| topic | Voltage control data-driven decentralized control multi-agent dynamic control real-time |
| url | https://ieeexplore.ieee.org/document/10679222/ |
| work_keys_str_mv | AT yulingwang datadrivenrealtimedynamicvoltagecontrolusingdecentralizedexecutionmultiagentdeepreinforcementlearning AT vijayvittal datadrivenrealtimedynamicvoltagecontrolusingdecentralizedexecutionmultiagentdeepreinforcementlearning |