Sustainable microgrids are powered by renewable energy sources [e.g., solar photovoltaic (PV) and wind energy], and these support the reliability, resilience, and the decarbonization of the electrical grid. In recent decades, advanced nonlinear control techniques are increasingly being used to integrate power converters to meet grid requirements. This study reviews the advanced nonlinear control techniques predominantly used for grid-connected converters, namely, data-driven control, nonlinear model predictive control, direct power control, sliding mode control, disturbance-observer methods, and passivity control. Recent advances in these control policies are highlighted and various design and performance features are compared. Solutions for grid-synchronization stability, nonideal and distorted grid conditions, circulating current suppression, power quality, harmonics suppression, and grid support are presented—as well as the future trends of the advanced control methods. The study indicates that advanced control offers advantages in dynamic performance, disturbance rejection and multiobjective control over linear control for multiobjective and multitimescale microgrids.

In this work, we propose an effective and simple control approach for islanded DC microgrids that allows each distributed generator (DG) to achieve accurate voltage regulation and power-sharing. An improved dynamic consensus protocol, which is robust to measurement noise and states initialization, is employed to enable each agent to locally calculate the average bus voltage with a sparse cyber network. On this basis, we propose a cooperative controller that merges the voltage regulation and power-sharing objectives in a unified fashion. The proposed approach only uses neighbors’ voltage information to regulates the average bus voltage to its nominal value while maintaining proportional power-sharing or optimal power dispatch. This significantly simplifies its implementation and reduces the communication bandwidth requirement. A global model of the DC microgrid considering the cyber network is established in the form of a state-space-model, where the reference voltage vector corresponds to the input and the average bus voltage vector denotes the state. Then, the input-to-state stability analysis is carried out. To the end, comprehensive hardware-in-the-loop (HiL) tests are conducted to validate the effectiveness of the proposed control strategy. The proposed control strategy exhibits plug-and-play capability, and it is resilient to message update rate and communication failure.

This paper investigates the adaptive event-triggered (AET) output feedback model predictive control (MPC) method for the load frequency control (LFC) problem of hybrid power system involving the thermal power and the photovoltaic power. First, an AET communication mechanism, which possesses the ability to dynamically adjust the trigger threshold parameter according to an adaptive law, is addressed to reducing the communication overhead, while keeping the required control performance; Second, the output feedback model predictive controller which is composed of an off-line designed state observer and an on-line solved optimization problem, is proposed using the techniques of quadratic boundedness and S-procedure. The state observer gain is obtained by off-line maximizing the trace of a matrix which is closely related to minimal positively invariant set; While the output feedback MPC strategy is acquired by expressing the infinite horizon control moves as a free control move and a linear feedback law based on the estimated state; Third, the recursive feasibility of the on-line optimization problem and the stability of the closed-loop system are analyzed by the fact that the estimated state and estimation error will converge to the presented minimal positively invariant set. Finally, MATLAB and its linear matrix inequality (LMI) toolbox are used to solve the optimization problem, and the LFC system with load disturbance is simulated and analyzed. The simulation results show that the proposed method is feasible and effective.

In this work, we develop a zero-sequence model to analyze the zero-sequence current, i.e., circulating current, in multiple paralleled power converters. On this basis, we propose and verify a novel and simple solution: predictive zero-sequence current control (PZSC $^2$ ) to freely mitigate the zero-sequence current. The proposed technique is implemented in a direct model predictive control framework via a synthesized voltage vector, which consists of an optimal shrunken voltage vector and a zero voltage vector. The former is selected to minimize the tracking error of the $\alpha \beta$ -axis current, while the latter is properly selected to mitigate the circulating current. Notably, the zero voltage vector is solely determined by the zero-sequence current direction, without using other converters’ operating knowledge. This permits the proposed PZSC $^2$ to operate in a self-governing manner, fitting the spatially distributed applications. We validate the proposed solution through a lab-constructed hardware test bench. Experimental results verify its effectiveness considering both steady-state and transient performance.

In this work, we propose a high-quality control solution for islanded microgrids with multi-parallel power converters; it uses a full state-variable direct model predictive control (FSV-DMPC) and has a simple structure. Unlike the conventional cascaded control loops, the proposed FSV-DMPC solution tracks the optimal reference generated by a robust droop loop using a unified cost function. This proposal enables the FSV-DMPC to be inserted into the entire control framework with plug-and-play capability; it is robust to parameter variations, while also guaranteeing dynamics and stability. We conduct a deep analysis of the proposed approach, taking into account both the characteristics of the solution and the bounded stability of the system. Through comprehensive comparative studies with a classical double-loop linear controller, we validate that our solution achieves superior output voltage regulation during the load transients in terms of voltage error and settling time. Meanwhile, similar steady-state performances are accomplished for both methods. Finally, we verify our approach experimentally in different scenarios through a lab-constructed microgrid test-bench. Experimental data confirm that the proposed approach achieves excellent steady-state and transient performances, and obtains accurate load sharing.