Search

With the increasing penetration of distributed energy resources into islanded microgrids, a grid-forming inverter (GFI) has become the key element interfacing renewable energy sources. Usually, the GFI is employed with an output filter to minimize the harmonic content achieving high-quality output voltage regulation. To this end, model-predictive control (MPC) has been widely proposed to control the output voltage of GFI systems due to the high-quality performance, fast control response, and straightforward handling of constraints. However, an accurate model of the system is required for the conventional MPC to avoid a suboptimal performance under uncertainties. To overcome this known drawback, a novel model-free predictive control is proposed in this article. Consequently, the output voltage of an $LCL$ -filtered GFI is regulated without the knowledge of the physical model. [ABSTRACT FROM AUTHOR]

The dynamic response speed of the secondary control (SC) in microgrids (MGs) is limited both by the communication network time delays and the bandwidth (BW) of the primary control (PC). By increasing the PC BW, which depends on how to design and tune the PC, the time delay issues will be multiplied due to the speed range of the communication modules and technologies. This article proposes a communication compensation block (CCB) to enhance the robustness of distributed SC against communication impairments in the MGs operated at the higher BW. The proposed method mitigates malicious time delays and communication non-ideality in distributed networked controls employed in the secondary layer of the MG by prediction, estimation and finally decision on transmitted data. A comprehensive mathematical model of the employed communication network is presented in details. Then, a robust data prediction algorithm based on the temporal and spatial correlation is applied into the SC to compensate for time delays and data packet loss. Furthermore, the effect of the number of stored packets and burst packet loss on the average success rate of the communication block, and the small signal stability analysis of the system in the presence of the CCB are investigated. Power hardware-in-the-loop (PHiL) experimental tests show the merits and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel secondary control strategy for the power-electronic-based ac microgrid. This approach restores the voltage and frequency deviations by utilizing only local variables with very high bandwidth. This is realized with a finite control set model predictive control technique that is adopted in the inner level of the primary control of voltage source converters. In the outer level of the primary control, droop control and virtual impedance loops are exploited to adjust the power sharing among different distributed generation (DGs). As inner control level operates with a very high bandwidth, need for filtering of the calculated active and reactive powers in the outer level of the primary control is insignificant. Therefore, the secondary control can be operated with a far superior bandwidth compared to the case when the conventional cascaded linear control is used. Merits of the proposed approach are investigated analytically with the help of the describing function methodology that allows the quasi-linear approximation of the inner control level. Finally, simulation and experimental results are presented. [ABSTRACT FROM AUTHOR]

This letter proposes a new modified model predictive control (MPC) to compensate for voltage and frequency deviations with higher bandwidth for an ac shipboard microgrid (MG). The shipboard power system (SPS) and islanded MGs have a reasonable analogy regarding supplying loads with local generations. However, a great number of vital imposing pulse loads and highly dynamic large propulsion loads in the SPS make the frequency and voltage regulation a complicated issue. Conventional linear control methods suffer from high sensitivity to parameter variations and slow transient response, which make big oscillations in the frequency and voltage of the SPS. This letter addresses the problem by proposing a novel finite control set MPC to compensate for primary frequency and voltage deviations with higher bandwidth and order of magnitude faster than the state of the art. Furthermore, a single input interval type-2 fuzzy logic controller is applied in secondary level to damp the steady-state deviations with higher bandwidth. Finally, hardware-in-the-loop experimental results prove the applicability of the proposed control structure. [ABSTRACT FROM AUTHOR]

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model's uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC. [ABSTRACT FROM AUTHOR]

Communication infrastructure (CI) in microgrids (MGs) allows for the application of different control architectures for the secondary control (SC) layer. The use of new SC architectures involving CI is motivated by the need to increase MG resilience and handle the intermittent nature of distributed generation units. The structure of SC is classified into three main categories, including centralized SC (CSC) with a CI, distributed SC (DISC) generally with a low-data-rate CI, and decentralized SC (DESC) with communication-free infrastructure. To meet the MGs’ operational constraints and optimize performance, control and communication must be utilized simultaneously in different control layers. In this survey, we review and classify all types of SC policies from CI-based methods to communication-free policies, including CSC, averaging-based DISC, consensus-based DISC methods, containment pinning consensus, event-triggered DISC, washout-filter-based DESC, and state-estimation-based DESC. Each structure is scrutinized from the viewpoint of the relevant literature. Challenges such as clock drifts, cyber-security threats, and the advantage of event-triggered approaches are presented. Fully decentralized approaches based on state-estimation and observation methods are also addressed. Although these approaches eliminate the need of any CI for the voltage and frequency restoration, during black start process or other functionalities related to the tertiary layer, a CI is required. Power hardware-in-the-loop experimental tests are carried out to compare the merits and applicability of different SC structures. [ABSTRACT FROM AUTHOR]

• State-of-the-art of model predictive control (MPC) of microgrids is reviewed. • The first paper in a two-part series on MPC of ac and dc microgrids. • Advantages of MPC for power quality, inertia emulation, transportation electrification. • Experimental comparison of MPC versus linear control for microgrid primary level. • Emerging trends of MPC for autonomous and networked microgrids. • Demonstration of new adaptive MPC of grid-forming converters. The smart-grid has requirements of flexible automation, efficiency, reliability, resiliency and scalability. These are necessitated by the increasing penetration of power-electronics converters that interface distributed renewable energy systems which energize the fast-evolving electric power network. Microgrids (MGs) have been identified as modular grids with the potential to effectively satisfy these characteristics when enhanced with advanced control capabilities. Model predictive control (MPC) facilitates the multivariable control of power electronic systems while accommodating physical constraints without the necessity for a cascaded structure. These features result in fast control dynamic response and good performance for systems involving non-linearities. This paper is a survey of the recent advances in MPC-based converters in MGs. Schemes for the primary control of MG parameters are presented. We also present opportunities for the MPC converter control of autonomous MGs (power quality and inertia enhancement), and transportation electrification. Finally, we demonstrate MPC's capabilities through hardware-in-the-loop (HiL) results for a proposed adaptive MPC scheme for grid-forming converters. [ABSTRACT FROM AUTHOR]

• Part-I reviews the model predictive control (MPC) of the primary level of microgrids. • Part-II reviews MPC in the secondary and tertiary control levels of microgrids. • Models for MPC in secondary centralized, decentralized and distributed architectures. The smart-grid has requirements of flexible automation, efficiency, reliability, resiliency and scalability. These are necessitated by the increasing penetration of power-electronics converters that interface distributed renewable energy systems which energize the fast-evolving electric power network. Microgrids (MGs) have been identified as modular grids with the potential to effectively satisfy these characteristics when enhanced with advanced control capabilities. Model predictive control (MPC) facilitates the multivariable control of power electronic systems while accommodating physical constraints without the necessity for a cascaded structure. These features result in fast control dynamic response and good performance for systems involving non-linearities. This paper is a survey of the recent advances in MPC-based converters in MGs. Schemes for the primary control of MG parameters are presented. We also present opportunities for the MPC converter control of autonomous MGs (power quality and inertia enhancement), and transportation electrification. Finally, we demonstrate MPC's capabilities through hardware-in-the-loop (HiL) results for a proposed adaptive MPC scheme for grid-forming converters. [ABSTRACT FROM AUTHOR]