Model predictive control of distributed generation micro-grids in island and grid connected operation under balanced and unbalanced conditions

Ghanbarian, Mohammad Mehdi; Nayeripour, Majid; Rajaei, Amirhossein; Jamshidi, Fatemeh; Waffenschmidt, Eberhard

doi:10.1063/1.4990823

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Continuous control set MPC (CCS-MPC) and FCS-MPC are two sets of MPC-based control at the converter level [103,104]. For controlling the converter output, the continuous signals are generated by CCS-MPC for the PWM regulator; contrarily, FCS-MPC is based on discrete signals, which remove the usage of any PWM modulator [34].…”

Section: Mpc-based Microgrid Controlmentioning

confidence: 99%

Microgrids with Model Predictive Control: A Critical Review

Joshal

Gupta

2023

Energies

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Microgrids face significant challenges due to the unpredictability of distributed generation (DG) technologies and fluctuating load demands. These challenges result in complex power management systems characterised by voltage/frequency variations and intricate interactions with the utility grid. Model predictive control (MPC) has emerged as a powerful technique to effectively address these challenges. By applying a receding horizon control strategy, MPC offers promising solutions for optimising constraints and enhancing microgrid operations. The purpose of this review paper is to comprehensively analyse the application of MPC in microgrids, covering various levels of the hierarchical control structure. Furthermore, this paper explores the emerging trend of employing MPC across microgrid applications, ranging from converter control levels for power quality to overarching energy management systems. It also investigates the future research perspectives by considering the challenges associated with establishing MPC-based microgrid control. The key conclusion derived from this review paper is that the implementation of MPC techniques in microgrid operations can greatly improve their overall performance, efficiency, and resilience. This paper thoroughly examines the various challenges faced in MPC-based microgrid operations, underscoring the significance of conducting research in advanced artificial intelligence (AI)-based MPC methods. It highlights how these cutting-edge AI techniques can bring about economic benefits in microgrid operations, addressing the complex demands of efficient energy management in a rapidly evolving landscape. The presented insights strive to enhance the comprehension and adoption of MPC techniques in microgrid settings, actively contributing to the ongoing improvement of their operational processes. By shedding light on key aspects and offering valuable guidance, this work aims to propel the advancement and effective utilisation of MPC methodologies in microgrids, ultimately leading to optimised performance and enhanced overall operations.

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Section: Mpc-based Microgrid Controlmentioning

confidence: 99%

Microgrids with Model Predictive Control: A Critical Review

Joshal

Gupta

2023

Energies

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“…Several control schemes have been introduced in order to ensure the proper operation of the microgrid in the grid connection mode or the islanding mode [17]. Moreover, several control schemes have been introduced in order to ensure the proper operation of the microgrid in the grid connection mode or the stand-alone mode [18]. A comprehensive review of dc microgrids can be found in [19][20][21].…”

Section: Study Of a Microgrid Containing A Dg Sourcementioning

confidence: 99%

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

Sayedi

Fatehi

Simab

2022

International Transactions on Electrical Energy Systems

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The distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future horizon of the system and its outputs. This predictability allows calculating the optimal order of inputs to minimize output errors over a limited horizon, which is effected by the limitations of the system. This study presents a distributed economic model predictive control method using the new state feedback controller to control the switching of interface converters and compensate for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB Simpower. Then, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and DSVM-MPC methods.

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Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks

Janko

Johnson

2020

Applied Energy

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Model predictive control of distributed generation micro-grids in island and grid connected operation under balanced and unbalanced conditions

Cited by 6 publications

References 24 publications

Microgrids with Model Predictive Control: A Critical Review

Microgrids with Model Predictive Control: A Critical Review

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks

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