Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid
This study introduces a Learning-based Load Frequency Control (LB-LFC) approach to manage the challenges posed by renewable energy’s intermittency in microgrids, which often causes load disturbances, frequency fluctuations, and higher generation costs. The LB-LFC method employs reinforcement learnin...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-02-01
|
| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0240774 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850236025318670336 |
|---|---|
| author | Xin Shen Yijing Zhang Jiahao Li Yitao Zhao Jianlin Tang Bin Qian Xiaoming Lin |
| author_facet | Xin Shen Yijing Zhang Jiahao Li Yitao Zhao Jianlin Tang Bin Qian Xiaoming Lin |
| author_sort | Xin Shen |
| collection | DOAJ |
| description | This study introduces a Learning-based Load Frequency Control (LB-LFC) approach to manage the challenges posed by renewable energy’s intermittency in microgrids, which often causes load disturbances, frequency fluctuations, and higher generation costs. The LB-LFC method employs reinforcement learning to balance generation costs and frequency stability effectively. In addition, a novel sort replay actor critic technique is proposed, leveraging the deep deterministic policy gradient algorithm and sort experience replay to enhance control efficiency and robustness. This dual-objective control strategy not only improves frequency management but also aims to reduce generation expenses. The effectiveness of this approach is validated through simulations on the isolated microgrid load frequency control model of China Southern Grid. |
| format | Article |
| id | doaj-art-b6a243bc9bce470e96e2167d2fbdc3c6 |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-b6a243bc9bce470e96e2167d2fbdc3c62025-08-20T02:02:04ZengAIP Publishing LLCAIP Advances2158-32262025-02-01152025026025026-1310.1063/5.0240774Novel efficient deep reinforcement learning-based load frequency control for isolated microgridXin Shen0Yijing Zhang1Jiahao Li2Yitao Zhao3Jianlin Tang4Bin Qian5Xiaoming Lin6Measurement Center, Yunnan Power Grid Co., Ltd., Kunming, ChinaMeasurement Center, Yunnan Power Grid Co., Ltd., Kunming, ChinaMeasurement Center, Yunnan Power Grid Co., Ltd., Kunming, ChinaMeasurement Center, Yunnan Power Grid Co., Ltd., Kunming, ChinaCSG Electric Power Research Institute Co., Ltd., Guangzhou, ChinaCSG Electric Power Research Institute Co., Ltd., Guangzhou, ChinaCSG Electric Power Research Institute Co., Ltd., Guangzhou, ChinaThis study introduces a Learning-based Load Frequency Control (LB-LFC) approach to manage the challenges posed by renewable energy’s intermittency in microgrids, which often causes load disturbances, frequency fluctuations, and higher generation costs. The LB-LFC method employs reinforcement learning to balance generation costs and frequency stability effectively. In addition, a novel sort replay actor critic technique is proposed, leveraging the deep deterministic policy gradient algorithm and sort experience replay to enhance control efficiency and robustness. This dual-objective control strategy not only improves frequency management but also aims to reduce generation expenses. The effectiveness of this approach is validated through simulations on the isolated microgrid load frequency control model of China Southern Grid.http://dx.doi.org/10.1063/5.0240774 |
| spellingShingle | Xin Shen Yijing Zhang Jiahao Li Yitao Zhao Jianlin Tang Bin Qian Xiaoming Lin Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid AIP Advances |
| title | Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid |
| title_full | Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid |
| title_fullStr | Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid |
| title_full_unstemmed | Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid |
| title_short | Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid |
| title_sort | novel efficient deep reinforcement learning based load frequency control for isolated microgrid |
| url | http://dx.doi.org/10.1063/5.0240774 |
| work_keys_str_mv | AT xinshen novelefficientdeepreinforcementlearningbasedloadfrequencycontrolforisolatedmicrogrid AT yijingzhang novelefficientdeepreinforcementlearningbasedloadfrequencycontrolforisolatedmicrogrid AT jiahaoli novelefficientdeepreinforcementlearningbasedloadfrequencycontrolforisolatedmicrogrid AT yitaozhao novelefficientdeepreinforcementlearningbasedloadfrequencycontrolforisolatedmicrogrid AT jianlintang novelefficientdeepreinforcementlearningbasedloadfrequencycontrolforisolatedmicrogrid AT binqian novelefficientdeepreinforcementlearningbasedloadfrequencycontrolforisolatedmicrogrid AT xiaominglin novelefficientdeepreinforcementlearningbasedloadfrequencycontrolforisolatedmicrogrid |