In this paper, a fixed-switching-frequency modulated model predictive control (M2PC) is established for a two-level three-phase voltage source inverter (VSI) working in an islanded AC microgrid. These small-scale power systems are composed by two or more VSIs which interface DGs, controlling the voltage amplitude and frequency in the system, and simultaneously sharing the load active and reactive power. Generally, these operational characteristics are achieved using hierarchical linear control loops, but with challenging limitations such as slow transient reaction to disturbances and high proneness to be affected by parameter modifications. Model predictive control may solve these issues. Nevertheless, the most used and developed predictive control scheme, the finite-set model predictive control (FS-MPC), presents the drawback of having the harmonic spectrum spread over all the frequencies. This brings issues with coupling between the different hierarchical control levels of the whole microgrid system, and eventually, when designing the filters for main-grid connection. This paper aims to solve these issues by developing the fixed-switching-frequency M2PC working with higher-level control loops for operation in an islanded AC microgrid. These advantages are proved in an AC microgrid configuration where methodology for paralleling multiple M2PC-regulated VSIs is described, with rapid transient response, inherent stability, and fully decentralised operation of individual VSIs, achieving proper load power sharing, eliminating circular currents, and proper waveforms for output currents and capacitor voltages. All these achievements have been confirmed via simulation and experimental verification.

Microgrids need control and management at different levels to allow the inclusion of renewable energy sources. In this paper, a comprehensive literature review is presented to analyse the latest trends in research and development referring to the applications of predictive control in microgrids. As a result of this review, it was found that the application of predictive control techniques on microgrids is performed for the three control levels and with adaptations of the models in order to include uncertainties to improve their performance and dynamics response. In addition, to ensure system stability, but also, at higher control levels, coordinated operation among the microgrid’s components and synchronised and optimised operation with utility grids and electric power markets. Predictive control appears as a very promising control scheme with several advantages for microgrid applications of different control levels.

This article proposes a 27-level asymmetric cascade H-bridge multilevel topology for photovoltaic applications, which considers a predictive control strategy that allows minimization of the commutations of the converter. This proposal ensures a highly sinusoidal and stable photovoltaic injection when there are solar irradiance disturbances, generating a low distortion in the current waveform and low switching losses. To validate the performance of the control and the proposed topology, the dynamic model of the alternating current (AC) and direct current (DC) side system is first obtained, which is checked by computational simulations. Subsequently, the implementation of a master–slave control is carried out, focused on the control of DC voltage and AC current. The proposal is simulated, and the total harmonic distortion (THD) is obtained in the voltage and current waveforms. Undesired commutations, typical of the predictive control, are eliminated in the AC voltage waveform, and a proper DC voltage tracking is achieved for the high-power cell. In order to demonstrate the performance of the proposed control strategy, a low-power proof-of-concept prototype is implemented, in which the energy is injected to the grid, under the event of solar irradiance disturbances (with DC control).Then, the undesired switching in the main cell is eliminated, generating THDs in the voltage and current signal of 7.76% and 2.65%, respectively.

Microgrids appear as the key part of the future power systems that include distributed generators, renewable energy, and energy storage. In this paper a decentralized power sharing control scheme that includes droop control with virtual impedances with PI controllers for the voltage and current is proposed for an islanded AC microgrid with two voltage source inverters in parallel that share a residential load. To avoid circulating currents and unbalanced power sharing due to line impedance differences in the microgrid, virtual impedances are added. The proposed control scheme is established in MAT-LAB/Simulink to prove the proper operation under inductive behavior and mismatches in the line impedances of the microgrid system.

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Predictive control has risen as a plausible option in the control of power converters for photovoltaic utilisation. In this work, a predictive control scheme is proposed to obtain the maximum power point in a photovoltaic array coupled directly to a grid-connected DC/AC power converter. The architecture considered avoids the use of the DC/DC stage, reducing the losses related to the inclusion of the boost stage. The introduced control strategy is proved by simulations in Matlab&Simulink, demonstrating flexibility to include the non-linear behaviour of the system and satisfying performance in terms of DC voltage and AC current control.

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The high increase of renewable energy sources and the increment of distributed generation in the electrical grid has made them complex and of variable parameters, causing potential stability problems to the PI controllers. In this document, a control strategy for power injection to the electrical system from photovoltaic plants through a voltage source inverter two-level-type (VSI-2L) converter is proposed. The algorithm combines a current-based maximum power point-tracking (Current-Based MPPT) with model predictive control (MPC) strategy, allowing avoidance of the use of PI controllers and lowering of the dependence of high-capacitive value condensers. The sections of this paper describe the parts of the system, control algorithms, and simulated and experimental results that allow observation of the behavior of the proposed strategy.