Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control
Abstract System reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LF...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-11-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12994 |
| _version_ | 1797640083515375616 |
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| author | Peixiao Fan Jun Yang Song Ke Yuxin Wen Yonghui Li Lilong Xie |
| author_facet | Peixiao Fan Jun Yang Song Ke Yuxin Wen Yonghui Li Lilong Xie |
| author_sort | Peixiao Fan |
| collection | DOAJ |
| description | Abstract System reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LFC) strategy for multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive control (MPC) is proposed. First, a controller‐interconnected multimicrogrid topology is proposed; thus, a multimicrogrid consisting of microturbines (MTs), distributed power sources, and EVs and their random power constraints is established. Second, a control parameter adaptive algorithm based on learning‐based MPC is designed. The real‐time frequency offset and EV station output power boundary are used as the state set, adjustable parameters of the MPC controller are used as the action set, and reward function is set with frequency deviation so that the adaptive adjustment of the weight parameters of the MPC controller is realised. Additionally, the improved MPC controller designed in this paper can transform the frequency control process into an optimization problem, which is well adapted to various random constraints in the control process. In addition, the deep deterministic policy gradient (DDPG)‐MPC double‐layer controller can prevent machine learning controller failure. Finally, the simulation results show that, compared with traditional control and MPC algorithms, the learning‐based MPC controller applied to the controller interconnection structure can exchange information between submicrogrids. Moreover, based on the experience accumulated in the prelearning process, the controller parameters can be updated according to the environmental state in real time, thereby significantly improving the robustness and rapidity of the multimicrogrid frequency control process. Meanwhile, compared with a traditional DDPG controller, the proposed controller with double‐layer coupling structure can better ensure the safe operation of the multimicrogrid system when the machine learning agent fails and cannot output actions normally. |
| first_indexed | 2024-03-11T13:26:44Z |
| format | Article |
| id | doaj.art-ae0a8729e5fa400c97e3c978b58a7800 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-11T13:26:44Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-ae0a8729e5fa400c97e3c978b58a78002023-11-03T06:13:53ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-11-0117214763478010.1049/gtd2.12994Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive controlPeixiao Fan0Jun Yang1Song Ke2Yuxin Wen3Yonghui Li4Lilong Xie5Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, School of Electrical Engineering and Automation, Wuhan UniversityWuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, School of Electrical Engineering and Automation, Wuhan UniversityWuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, School of Electrical Engineering and Automation, Wuhan UniversityWuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, School of Electrical Engineering and Automation, Wuhan UniversityWuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, School of Electrical Engineering and Automation, Wuhan UniversityWuhan ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, School of Electrical Engineering and Automation, Wuhan UniversityWuhan ChinaAbstract System reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LFC) strategy for multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive control (MPC) is proposed. First, a controller‐interconnected multimicrogrid topology is proposed; thus, a multimicrogrid consisting of microturbines (MTs), distributed power sources, and EVs and their random power constraints is established. Second, a control parameter adaptive algorithm based on learning‐based MPC is designed. The real‐time frequency offset and EV station output power boundary are used as the state set, adjustable parameters of the MPC controller are used as the action set, and reward function is set with frequency deviation so that the adaptive adjustment of the weight parameters of the MPC controller is realised. Additionally, the improved MPC controller designed in this paper can transform the frequency control process into an optimization problem, which is well adapted to various random constraints in the control process. In addition, the deep deterministic policy gradient (DDPG)‐MPC double‐layer controller can prevent machine learning controller failure. Finally, the simulation results show that, compared with traditional control and MPC algorithms, the learning‐based MPC controller applied to the controller interconnection structure can exchange information between submicrogrids. Moreover, based on the experience accumulated in the prelearning process, the controller parameters can be updated according to the environmental state in real time, thereby significantly improving the robustness and rapidity of the multimicrogrid frequency control process. Meanwhile, compared with a traditional DDPG controller, the proposed controller with double‐layer coupling structure can better ensure the safe operation of the multimicrogrid system when the machine learning agent fails and cannot output actions normally.https://doi.org/10.1049/gtd2.12994controller interconnectiondeep deterministic policy gradient (DDPG)electric vehicleslearning‐based MPCmultimicrogrids frequency controloperational safety |
| spellingShingle | Peixiao Fan Jun Yang Song Ke Yuxin Wen Yonghui Li Lilong Xie Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control IET Generation, Transmission & Distribution controller interconnection deep deterministic policy gradient (DDPG) electric vehicles learning‐based MPC multimicrogrids frequency control operational safety |
| title | Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control |
| title_full | Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control |
| title_fullStr | Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control |
| title_full_unstemmed | Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control |
| title_short | Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control |
| title_sort | load frequency control strategy for islanded multimicrogrids with v2g dependent on learning based model predictive control |
| topic | controller interconnection deep deterministic policy gradient (DDPG) electric vehicles learning‐based MPC multimicrogrids frequency control operational safety |
| url | https://doi.org/10.1049/gtd2.12994 |
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