Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints
In this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/3/1170 |
| _version_ | 1797487901669326848 |
|---|---|
| author | Xiaogang Dong Jinqiang Gan Hao Wu Changchang Deng Sisheng Liu Chaolong Song |
| author_facet | Xiaogang Dong Jinqiang Gan Hao Wu Changchang Deng Sisheng Liu Chaolong Song |
| author_sort | Xiaogang Dong |
| collection | DOAJ |
| description | In this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the control signals generated by distributed secondary control algorithm are used to solve the problems of voltage and frequency recovery and power allocation for each distributed generators (DGs). Therefore, the model predictive control (MPC) with the mechanism of rolling optimization is adopted in the second control layer to achieve the above control objectives and solve the physical state constraint problem at the same time. Meanwhile, in order to reduce the communication cost, we designed the self-triggered control based on the prediction mechanism of MPC. In addition, the proposed algorithm of self-triggered MPC does not need sampling and detection at any time, thus avoiding the design of observer and reducing the control complexity. In addition, the Zeno behavior is excluded through detailed analysis. Furthermore, the stability of the algorithm is verified by theoretical derivation of Lyapunov. Finally, the effectiveness of the algorithm is proved by simulation. |
| first_indexed | 2024-03-09T23:55:22Z |
| format | Article |
| id | doaj.art-4f0c4ef979e042749cc85580069e98c7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T23:55:22Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4f0c4ef979e042749cc85580069e98c72023-11-23T16:26:27ZengMDPI AGEnergies1996-10732022-02-01153117010.3390/en15031170Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State ConstraintsXiaogang Dong0Jinqiang Gan1Hao Wu2Changchang Deng3Sisheng Liu4Chaolong Song5School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaIn this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the control signals generated by distributed secondary control algorithm are used to solve the problems of voltage and frequency recovery and power allocation for each distributed generators (DGs). Therefore, the model predictive control (MPC) with the mechanism of rolling optimization is adopted in the second control layer to achieve the above control objectives and solve the physical state constraint problem at the same time. Meanwhile, in order to reduce the communication cost, we designed the self-triggered control based on the prediction mechanism of MPC. In addition, the proposed algorithm of self-triggered MPC does not need sampling and detection at any time, thus avoiding the design of observer and reducing the control complexity. In addition, the Zeno behavior is excluded through detailed analysis. Furthermore, the stability of the algorithm is verified by theoretical derivation of Lyapunov. Finally, the effectiveness of the algorithm is proved by simulation.https://www.mdpi.com/1996-1073/15/3/1170AC microgridsmodel predictive controlself-triggeredphysical and communication state constraints |
| spellingShingle | Xiaogang Dong Jinqiang Gan Hao Wu Changchang Deng Sisheng Liu Chaolong Song Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints Energies AC microgrids model predictive control self-triggered physical and communication state constraints |
| title | Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints |
| title_full | Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints |
| title_fullStr | Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints |
| title_full_unstemmed | Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints |
| title_short | Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints |
| title_sort | self triggered model predictive control of ac microgrids with physical and communication state constraints |
| topic | AC microgrids model predictive control self-triggered physical and communication state constraints |
| url | https://www.mdpi.com/1996-1073/15/3/1170 |
| work_keys_str_mv | AT xiaogangdong selftriggeredmodelpredictivecontrolofacmicrogridswithphysicalandcommunicationstateconstraints AT jinqianggan selftriggeredmodelpredictivecontrolofacmicrogridswithphysicalandcommunicationstateconstraints AT haowu selftriggeredmodelpredictivecontrolofacmicrogridswithphysicalandcommunicationstateconstraints AT changchangdeng selftriggeredmodelpredictivecontrolofacmicrogridswithphysicalandcommunicationstateconstraints AT sishengliu selftriggeredmodelpredictivecontrolofacmicrogridswithphysicalandcommunicationstateconstraints AT chaolongsong selftriggeredmodelpredictivecontrolofacmicrogridswithphysicalandcommunicationstateconstraints |