Model Predictive Load Frequency Control of Isolated Micro-Grid with Electrical Vehicles

Pan, Jingjun; Yu, Shaoyuan; Ma, Miaomiao

doi:10.23919/chicc.2018.8482678

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…Charging operation of EV using MPC mechanism [72] The proposed MPC-based control for EV charging operations leads to the EV users taking part in management of demand-side programs; this will enable the EV users to help in enhancing the efficiency and stability of the smart grid Multiple model predictive control (MMPC) for plugin hybrid EV (PHEV) [73] Compared to MPC and PID controllers, the proposed MMPC mechanism for PHEV charging/discharging provides better state of charge (SoC) with reduced frequency deviations and robust behaviour to uncertainties within the microgrid system Electric Vehicle (EV) Integration [72][73][74][75][76] MPC for LFC in microgrid with EVs [74] The Proposed MPC mechanism for the load frequency control problem in the microgrid with EVs shows zero frequency deviations while addressing the constraints related to EVs and DGs MPC for three-level buck/boost converter [75] Implemented the integration of an EV charging station (within dc microgrid) by proposing a controller based on a bidirectional three-level buck/boost converter with MPC; the mitigation of imbalance in voltage as well as issues related to voltage regulation in a bipolar DC microgrid are dealt with for the proposed controller with faster response MPC and adaptive droop control for ESS and EVs [76] The frequency deviation problem in an isolated microgrid, caused by renewable integration or sudden load demand variations, is dealt with using MPC and adaptive droop control for ESS and EVs; the proposed controller with better performance is compared with PI as well as with a fuzzy-logic-based PI controller…”

Section: Area Of Research References Technique Achievements/contributionmentioning

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: Area Of Research References Technique Achievements/contributionmentioning

confidence: 99%

Microgrids with Model Predictive Control: A Critical Review

Joshal

Gupta

2023

Energies

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“…As highlighted by the papers previously mentioned, MPC is used in recent years for voltage and frequency regulation, distributed generation management, EV and ESS management, or power flow management. Some recent papers are highlighted in the followings in order to give to the reader an overview on how MPC is used for MG/NMG management [59][60][61][62][63][64][65][66][67][68][69][70][71].…”

Section: Model-based Predictive Controlmentioning

confidence: 99%

“…The effectiveness of the proposed controller is demonstrated via numerical simulations. Using MPC, frequency deviation has been reduced to zero by [65] in an islanded MG. Diesel generators and EVs has been put to good use. In [66], a new control strategy based on MPC with seamless transfer characteristics for a grid-connected voltagesource inverter is presented.…”

Section: Model-based Predictive Controlmentioning

confidence: 99%

A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

2022

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Microgrids (MGs) and networked (interconnected) microgrids (NMGs) are emerging as an efficient way for integrating distributed energy resources (DERs) into power distribution systems. MGs and NMGs can disconnect from the main grid and operate autonomously, strengthen grid resilience, and help mitigate grid disturbances and maintain power quality. In addition, when supported by sophisticated and efficient management strategies, MGs and NMGs have the ability to enhance power supply reliability. However, their deployment comes with many challenges, in particular regarding the efficient management of DERs. That is why a survey of recent advances in the smart management—the term refers to a variety of planning and control tasks—of MGs and NMGs is presented in this paper. It aims at establishing a picture of strategies and identifying trends in methods. The reader is provided with an in-depth analysis of a variety of papers recently published in peer-reviewed journals: the way the methods are used and the common issues addressed by the scientific community are discussed. Following this analysis, one can especially observe that (1) model-based predictive control (MPC) is emerging as a competitive alternative to conventional methods, in particular in voltage and frequency regulation and DER management (2) due to their ability to handle complex tasks, data-driven strategies are getting more and more attention from the scientific community (3) game theory (GT) is a very good candidate for efficient management of complex systems as NMGs (4) MPC and artificial intelligence are increasingly being used for proper MG islanded operation or to manage electric vehicles (EVs) efficiently.

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Designing optimized PID controller using improved bacterial foraging optimization algorithm for robust frequency control of islanded microgrid

Luo

2022

Int. J. Dynam. Control

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Model Predictive Load Frequency Control of Isolated Micro-Grid with Electrical Vehicles

Cited by 10 publications

References 12 publications

Microgrids with Model Predictive Control: A Critical Review

Microgrids with Model Predictive Control: A Critical Review

A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

Designing optimized PID controller using improved bacterial foraging optimization algorithm for robust frequency control of islanded microgrid

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