Model Predictive Control Based DC Microgrid Virtual Inertia Optimal Method
Zhao Shuqiang1, Wang Hui1, Tian Na2, Meng Jianhui1, Wang Chen1, Tian Yanjun1
1. Key Laboratory of Distributed Energy Storage and Microgrid of Hebei Province North China Electric Power University Baoding 071003 China; 2. Economic Research Institute of State Grid Hebei Electric Power Company Shijiazhuang 050024 China
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, it is difficult to provide the design details for the "optimal" parameters. Moreover, the virtual inertia is determined by the operation state, and the controller acts in a delayed manner with the system variations, therefore due to dynamic voltage constraints, the emergency preventive control cannot be realized. In order to solve the problems, a model predictive controller has been proposed to realize the virtual inertia optimization of the DC microgrid, which is achieved by dynamically adjusting the units virtual inertia coefficients. Firstly, the linear discrete model of DC microgrid with virtual inertial control units was established, which is to predict the future trend of system output. Secondly, the model predictive controller was designed, which is designed to minimize the weighted sum of DC voltage tracking and virtual inertia, maintain the DC voltage stable and the intensity of the inertial response acceptable. Taking the DC voltage and its climbing rate as the constraints, preventing the excess of safety threshold for the dynamic voltage is also the key point, 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, so as to realize the optimization of virtual inertia. Finally, the selection principles of controller main parameters are given through the theoretical analysis. To verify the feasibility and effectiveness of the proposed method, hardware-in-the-loop simulation experiments have been built. Compared with the adaptive virtual inertial control, the test result shows that the proposed method provides a stronger inertial support for the system, increasing rapidly in the initial stage of the sudden load increase , reducing 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 the steady state and its climbing rate within a desired range. Finally, the model mismatch scenarios have also been tested, and result shows that, under the line model mismatch, the performance of DC bus voltage and virtual inertia coefficients do not degrade substantially, which is achieved by the compensation of the optimized rolling, thus guarantee the controller adaptive performance. Conclusions can be drawn that: (1) The proposed method can improve the stability, dynamic characteristics and safety operation of microgrid DC bus voltage. Aiming at the minimizing of 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 is capable to reduce the optimization size problem, which is beneficial to optimize the computational burden of the MPC controller. By simplifying the DC microgrid line model and converter control loops, the prediction model can be effectively simplified and well controlled, thus reducing the optimization scale, 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 and re-solve it, which mitigates the model uncertainty caused by mismatch.
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