Stabilization of Constant Power Loads in DC Microgrid Systems Using an Adaptive Continuous Control Set Model Predictive Control

Abstract

This paper represents an adaptive continuous control set model predictive control (CCS-MPC) to solve the disturbance-caused instability problems in a DC microgrid consisting of symmetrical parallel buck converters, constant voltage loads (CVL), and constant power loads (CPL). The symmetric model of the system is founded at first to describe and analyze the disturbance-caused instability problem. To mitigate the instability by taking the disturbances into consideration, the proposed adaptive controller is made from a double loop feedback controller and a feedforward estimation algorithm. In the voltage loop of the double loop controller, a capacitor dynamic-based voltage controller is developed, while in the current loop, a CCS-MPC algorithm is modified and applied. Meanwhile, a feedforward algorithm is developed to estimate the disturbance information and send it to the double loop controller to improve its robustness, so the composite controller can maintain the bus voltage fixed at its reference and the symmetrical equal current sharing. Finally, comparative MATLAB simulations and OPAL-RT hardware-in-the-loop experiments are conducted to verify the effectiveness and dynamic performance of the proposed algorithm towards other previous nonlinear controllers.

Control Strategies and Stabilization Techniques for DC/DC Converters Application in DC MGs: Challenges, Opportunities, and Prospects—A Review

“…MPC optimizes control by minimizing a cost function within a selected control horizon, employing a forecasting perspective [30]. It operates in a real-time feedback loop, incorporating voltage and current limitations to enhance voltage regulation, power flow management, reliability, and efficiency in DC MGs with variable loads [31,32].…”

Model Predictive Control Strategies in Microgrids: A Concise Revisit

“…Zhou et al stabilized the constant power needs in DC microgrid systems with the help of an adaptive continuous control set model predictive control. Although the recommended method provides improved dynamic performance, it exerts a larger computational burden on the CPU [48]. To ensure the stability and resilience of buck power converters in DC microgrids, Zhou et al devised a unique continuous control model for predictive control.…”

DC Bus Voltage Influence and Stabilization Technology Under Servo Loading for Aerospace Combined Power Supply

“…The predicted value of the current state can be obtained from the above two formulas. Combined with the measured value of the current state, the optimal estimated value X(k|k) of the current state (k) can be obtained: (17) where Kg is the Kalman gain, with its size given by: (18) When the Kalman filter has run to the end of the system, the k state covariance of X(k|k) also needs to be constantly updated: (19) where I is the identity matrix. Equations ( 15) to (19) are summarised according to the Kalman filter principle, according to which the computer software program can be written.…”

“…In [18], a coordinated control strategy with two non-governor diesel generators and two battery packs as marine power sources has been proposed, however, it only considered the two operation modes of DC SMG studied. In [19], an adaptive continuous control set model predictive control (CCSMPC) has been proposed to solve the perturbationinduced instability problem in a DC microgrid composed of symmetrical parallel buck converters, constant voltage loads (CVL) and constant power loads (CPL), but the stability control of boost converter is not involved.…”