Microgrids are the cornerstone for a new model of electrical generation based on renewable resources. Commonly microgrids are controlled with a centralised hierarchical structure, which is inherited from power systems. However, a time-scale separation between traditional fast frequency restoration and slow economic dispatch may be counterproductive in the long run because the slow long-term economic dispatch increases the prediction uncertainty. In an effort to improve the economical operation of microgrids, this work proposes a distributed model predictive control strategy for the operation of isolated microgrids based on a consensus strategy that tackles both the economic dispatch and frequency restoration over the same time-scale. The proposed controller can operate without knowledge of the microgrid's topology: instead, typical local measurements and other information from neighboring generation units are required. Experimental results demonstrate that the controller is robust to load variations and communication issues, but the plug-and-play nature of the system is preserved.

This paper proposes a distributed predictive secondary control strategy to share imbalance in three-phase, three-wire isolated AC Microgrids. The control is based on a novel approach where the imbalance sharing among distributed generators is controlled through the control of single-phase reactive power. The main characteristic of the proposed methodology is the inclusion of an objective function and dynamic models as constraints in the formulation. The controller relies on local measurements and information from neighboring distributed generators, and it performs the desired control action based on a constrained cost function minimization. The proposed distributed model predictive control scheme has several advantages over solutions based on virtual impedance loops or based on the inclusion of extra power converters for managing single-phase reactive power among distributed generators. In fact, with the proposed technique the sharing of imbalance is performed directly in terms of single-phase reactive power and without the need for adding extra power converters into the microgrid. Contrary to almost all reported works in this area, the proposed approach enables the control of various microgrid parameters within predefined bands, providing a more flexible control system. Extensive simulation and Hardware in the Loop studies verify the performance of the proposed control scheme. Moreover, the controller’s scalability and a comparison study, in terms of performance, with the virtual impedance approach were carried out.

This paper presents a novel scheme based on distributed model-based predictive control for the secondary level control of hybrid AC/DC microgrids. Prediction models based on droop control and power transfer equations are proposed to characterize the generators in both the AC and DC sub-microgrids, whereas power balance constraints are used to predict the behavior of interlinking converters. The operational constraints (such as powers and control action limits) are included in all the formulations. Experimental results validate the proposed scheme for the following cases: (i) load changes, working within operating constraints, (ii) managing frequency regulation in the AC sub-microgrid, voltage regulation in the DC sub-microgrid and global power consensus in the whole hybrid microgrid, and (iii) maintaining the microgrid performance in the presence of communication malfunction while ensuring that plug-and-play capability is preserved.

Hybrid AC/DC microgrids (H-MGs) are a prominent solution for integrating distributed generation and modern AC and DC loads. However, controlling these systems is challenging as multiple electrical variables need to be controlled and coordinated. To provide flexibility to the control system, these variables can be regulated to specific values or within secure bands. This paper proposes a set of distributed model predictive control schemes for the secondary control level to control certain variables to specific values and other variables within secure pre-defined bands into H-MGs. Specifically, optimal dispatch of active and reactive power is achieved while frequency and voltages are regulated within secure bands in H-MGs. Dynamic models of AC generators, DC generators and interlinking converters along with their novel multi-objective cost functions are developed in constrained distributed predictive optimisation problems to simultaneously achieve the aforementioned objectives via information sharing. Extensive simulation work validates the performance of this proposal.

The modular multilevel converter (MMC) is a prominent solution for medium- to high-voltage and high-power conversion applications. Recently, distributed control strategies have been proposed to make this converter modular in terms of software and control hardware. In this control architecture, high-level control tasks are performed by a central controller (CC), whereas low-level control tasks are achieved by local controllers (LCs) placed on the MMC submodules. The CC and LCs use a cyber-physical system (CPS) to share all the necessary information to execute their respective control schemes. In this context, the CPS is vulnerable to cyberattacks, such as the false data injection attack (FDIA), where the data seen by the controllers are corrupted through illegitimate data intrusion. This cyberattack may hinder the MMC performance, producing suboptimal, or even unstable operations. Even more, diverse FDIAs can be generated using artificial intelligence methods to deceive FDIA detectors. This article proposes an FDIA detector based on the reinforcement learning (RL) technique to detect sophisticated FDIAs targeting the MMC control system. The performance of the proposed RL-based FDIA detector is verified via hardware-in-the-loop studies, showing its effectiveness in detecting sophisticated attack sequences affecting the MMC control system.