Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

García-Torres, Félix; Zafra-Cabeza, Ascensión; Silva, Carlos; Grieu, Stéphane; Darure, Tejaswinee; Estanqueiro, Ana

doi:10.3390/en14051296

Cited by 47 publications

(16 citation statements)

References 102 publications

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“…Microgrids that are dominated by solar PV use converter-level and grid-level MPC to control and manage power flow (Hu et al, 2021). MPC comprises aspects such as cost function and solving algorithms for the optimization of critical parameters like active and reactive power regulation, harmonic reduction, and ripple minimization in microgrid systems (Garcia-Torres et al, 2021). Quadratic programming was used by (Wibowo et al, 2017) to economically dispatch power inside a hybrid microgrid system.…”

Section: Microgrid Controlmentioning

confidence: 99%

A review of modeling and simulation tools for microgrids based on solar photovoltaics

2022

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Solar Photo Voltaic (PV) powered community microgrids are a promising sustainable solution for neighborhoods, residential quarters, and cities in sub-Saharan Africa (SSA) to meet their energy demands locally and to increase energy independence and resilience. This review provides a comprehensive study on the nature of solar PV community microgrids. Through their capacity to operate in both grid-connected and island modes, community microgrids improve utility system resiliency while also boosting energy security in local states and towns. The integration of solar PV microgrids with the electricity utility grid requires control strategies to facilitate the load sharing between distributed generation units, voltage and frequency control, as well as emergency islanding. Control strategies such as hierarchical control and droop are discussed in the review article. To identify the effectiveness of control strategies through system simulation, a review of various modeling designs of individual components in a solar PV microgrid system is discussed. The article goes on to talk about energy optimization approaches and their economic impact on microgrid systems. Finally, the review concludes with an overview of the technical challenges encountered in the integration of solar PV systems in microgrids.

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Section: Microgrid Controlmentioning

confidence: 99%

A review of modeling and simulation tools for microgrids based on solar photovoltaics

2022

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“…For frequency and voltage regulation, model-based controllers have been proposed. Among these model-based controllers, model predictive control (MPC) offers many advantages in microgrids because of its flexibility to include constraints and nonlinearities in a systematic way [39]. However, the major drawback of the MPC is the model of the microgrid.…”

Section: Microgridsmentioning

confidence: 99%

Model-Free Predictive Control and Its Applications

2022

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Predictive control offers many advantages such as simple design and a systematic way to handle constraints. Model predictive control (MPC) belongs to predictive control, which uses a model of the system for predictions used in predictive control. A major drawback of MPC is the dependence of its performance on the model of the system. Any discrepancy between the system model and actual plant behavior will greatly affect the performance of the MPC. Recently, model-free approaches have been gaining attention because they are not dependent on the system model parameters. To obtain the advantages of both a model-free approach and predictive control, model-free predictive control (MFPC) is being explored and reported in the literature for different applications such as power electronics and electric drives. This paper presents an overview of model-free predictive control. A comprehensive review of the application of MFPC in power converters, electric drives, power systems, and microgrids is presented in this paper. Moreover, challenges, opportunities, and emerging trends in MFPC are also discussed in this paper.

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“…MPC is a method of constrained control that is based on the principles of feedback structures and numerical optimization [109]. Through the usage of a system that is capable of capturing the involved dynamics, MPC uses this tool to predict future states and select an optimized control action that can accomplish a determined performance index and defined constraints [110].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

et al. 2021

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This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

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Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

Cited by 47 publications

References 102 publications

A review of modeling and simulation tools for microgrids based on solar photovoltaics

A review of modeling and simulation tools for microgrids based on solar photovoltaics

Model-Free Predictive Control and Its Applications

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

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