基于模型预测控制的直流微电网虚拟惯性优化方法

doi:10.19595/j.cnki.1000-6753.tces.220691

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Abstract To improve the stability and dynamic characteristics of microgrid DC bus voltage, researchers have proposed adaptive virtual inertial control methods. These methods have clear analytical formulas for virtual inertia, but providing the design details for the “optimal” parameters is challenging. Moreover, the operation state determines the virtual inertia, and the controller acts in a delayed manner with the system variations. As a result, emergency preventive control cannot be realized due to dynamic voltage constraints. Therefore, a model predictive controller has been proposed to realize the virtual inertia optimization of the DC microgrid by dynamically adjusting the units’ virtual inertia coefficients.
Firstly, the linear discrete model of a DC microgrid with virtual inertial control units was established to predict the future trend of system output. Secondly, the model predictive controller was designed to minimize the weighted sum of DC voltage tracking and virtual inertia, maintain the DC voltage stability, and make the intensity of the inertial response acceptable. Taking the DC voltage and its climbing rate as the constraints, preventing the excess safety threshold for the dynamic voltage is also crucial in case of machine cut-off, load cut-off, or even system collapse. At each sampling period, the individual virtual inertia coefficient is solved and updated to realize the optimization of virtual inertia. Finally, the selection principles of the main parameters are given through theoretical analysis.
Hardware-in-the-loop simulation experiments have been built to verify the proposed method. Compared with the adaptive virtual inertial control, the proposed method provides a stronger inertial support for the system, increases rapidly in the initial stage of the sudden load increase, and reduces the drop of DC bus voltage. In reverse recovery, the virtual inertia coefficients were reduced, so the DC voltage recovery is accelerated in the beginning, and the recovery speed is reduced in the following time to eliminate the overshoot. In addition, the test result shows that the proposed method can maintain a steady state and its climbing rate within the desired range. Finally, the model mismatch scenarios have also been tested. The result shows that, under the line model mismatch, the performance of DC bus voltage and virtual inertia coefficients do not degrade substantially by compensating for the optimized rolling, thus guaranteeing the controller’s adaptive performance.
Conclusions can be drawn: (1) The proposed method can improve the stability, dynamic characteristics, and safe operation of microgrid DC bus voltage. Aiming at minimizing DC voltage tracking error and virtual inertia coefficients, the proposed control has advantages in improving DC bus voltage stability, accelerating recovery speed, and reducing overshoot. It can also prevent the voltage and its climbing rate out of the thresholds. (2) The proposed method can reduce the optimization size problem, which is beneficial for optimizing the computational burden of the MPC controller. The prediction model can be effectively simplified and well controlled by simplifying the DC microgrid line model and converter control loops, thus reducing the optimization scale and contributing to accelerated calculation speed. (3) The proposed method provides certain tolerance to line model mismatch. MPC uses a rolling optimization mechanism to update the optimization problem with updated measured values, which mitigates the model uncertainty caused by the mismatch.

Key words： DC microgrid virtual inertia optimal control linear discrete model model prediction control

Received: 27 April 2022

PACS:

TM712

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	Articles by authors
	Zhao Shuqiang
	Wang Hui
	Tian Na
	Meng Jianhui
	Wang Chen
	Tian Yanjun

Cite this article:

Zhao Shuqiang,Wang Hui,Tian Na等. Model Predictive Control Based DC Microgrid Virtual Inertia Optimal Method[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3264-3276.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.220691 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I12/3264

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