The conventional model predictive control (MPC) suffers from high common-mode voltage (CMV) magnitude and large current ripples. In this paper, the reduced CMV MPC strategy with double vector (RCMV-MPCDV) is proposed for three-level inverters, which adopts double vector to reduce the CMV and current ripple simultaneously in per-sampling period. Firstly, based on geometric relationship of median, a novel cost function that evaluate median line distance is proposed to reduce the computational complexity, rather than evaluating the closest distance. Secondly, in order to reduce the computational burden, the double vector preselection (DVP) algorithm is presented with fewer evaluation times. Thirdly, the candidate vectors are reclassified and regrouped from partial basic vectors, which restricts CMV magnitude within one-sixth of DC-link voltage. In addition, to select more satisfactory double vectors for RCMV-MPCDV scheme, two improved methods are proposed to compensate the losing candidate vectors. The simulation and experimental results with steady-state performance and dynamic response validate the effectiveness of the proposed method.

Paralleled three-level T-type inverters face the unavoidable pitfall of the zero-sequence circulating current (ZSCC). Although the conventional model predictive control (MPC) could eliminate the ZSCC, the control error occurs inevitably since only one vector is adopted in each sampling period, which decreases the current control performance. Thus, simultaneous ZSCC elimination and accurate current tracking become imperative to the inverter using MPC. This article proposes a multivector MPC to eliminate the ZSCC and reduce the current distortion without the weighting factor. As the common-mode voltage (CMV) and neutral point (NP) voltage are the main factors that induce the ZSCC, the virtual vector with zero-average CMV is constructed to eliminate the ZSCC. Meanwhile, the small vector is introduced to improve the NP voltage control capability. In addition, to enhance the current accuracy and reduce the ZSCC ripple, three optimal vectors are adopted to synthesize desired voltage. Moreover, the multivector preselection algorithm is proposed to reduce the computational complexity of implementation process. Finally, the effectiveness of the proposed method is verified with comprehensive simulation and experimental results.

The microgrid (MG) which effectively utilizes distributed energy resources (DERs) is crucial to the modern power system. However, since the islanded MG with high penetration of DERs inherently lacks sufficient inertial support, the frequency regulation is challenging for the voltage source converter (VSC)-based MG. This paper presents a cascaded model predictive control (MPC) scheme for both the outer and inner loops of primary control, which improves frequency regulation capability with the advantage of satisfactory dynamic response and high tracking accuracy. In the outer loop, to enhance dynamic frequency characteristics, the MPC-based virtual synchronous generator (VSG) method with two control objectives is proposed. According to the movement direction of frequency under different load switching cases, the corresponding frequency response process speeded up or slowed down properly. In the inner loop, an improved finite-set double vector MPC (DV-MPV) is proposed to follow the output reference from the outer loop, which contributes to accurate frequency control by reducing tracking error, and faster dynamic response as well. The simulation and experimental results further demonstrated the effectiveness of the proposed method.

AC/DC power converter is the momentous interface of the power router, which is responsible for maintaining the DC bus voltage stability. However, under unbalanced and distorted grid voltage conditions, there are voltage ripples in the DC bus, and grid currents will also be severely distorted. Accordingly, this paper proposes an improved passivity-based direct power control (IPBDPC) strategy based on extended reactive power theory in the $\alpha \beta$ axis, which can effectively suppress DC voltage ripples and current harmonics of power converter. Meanwhile, with the proposed IPBDPC, the fundamental grid voltages and those orthogonal components are injected into the improved passivity model, so that the controller can further reduce the THD of the grid current when unbalanced and distorted grid conditions coexist. Compared with the traditional modified DPC, the IPBDPC strategy has a simple structure and strong robustness, does not require complex positive and negative sequence separation, and power compensation value calculation. Moreover, the sequence impedance model of IPBDPC is constructed based on the harmonic linearization method to verify the robustness of the control system against parameters uncertainty. The simulation and experimental results based on a three-phase three-level converter system further demonstrated the effectiveness of the proposed control strategy.

The integrated community energy system (ICES) can realize the economic and environmental protection. As the critical equipment for ICES, energy router (ER) can realize the flexible transformation of different energy carriers. However, the efficiencies of the energy conversion devices in ER are usually assumed to be constant for the sake of simplicity, which may result in out-of-order operation schemes and redundant costs of ICES. In addition, the forms of multiple energy coupling of ICES are complex, and the conventional methods are difficult to coordinate the multiple energy demands. This paper proposes a demand side management (DSM) method for ICES. Firstly, a dynamic ER model is built to determine the energy conversion efficiency with the variation in load rate. The energy hub model provides a mathematical representation of ER, considering energy demand and the availability of different energy sources. Then, the DR considering renewable energy output is proposed, where a real-time price strategy is established and determined by the power of load demand and generation of renewable energy. Finally, a multi-objective operation optimization strategy based on low-carbon and the economy is established. The model involves the device constraint in the process of energy production and energy transmission. The simulation results of the proposed model under different scenarios are compared. The results show that the model effectively improves the economic benefits of ICES and reduces carbon emissions, and alleviates the pressure on the energy supply.

This article proposes a double vector model predictive control (MPC) method for the $LC$ filtered three-level T-type inverter (3LT $^{2}$ I) to gain superior voltage performance, which is current sensorless and computationally efficient. In the proposed scheme, inverter voltage increment is induced in the multi-objectives cost function, and the desired voltage is derived based on the simplified discrete model, which optimizes the output voltage control performance. The prepositive prediction calculation is shifted out from the evaluation circle, and the computation is lessened simultaneously. Then, aiming to estimate the capacitor current of the simplified discrete model with voltage feedback only, the strong-tracking Kalman filter (STKF)-based observer is designed, which gets rid of the dependence on current sensors and lowers hardware cost. Furthermore, to improve the control accuracy of tracking the desired voltage, a two-layer decision algorithm based on the search tree is developed to find the optimal double vector easily, which obtains a low-complexity selection process using the univariate cost function. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the theoretical analyses and the proposed method.