Model predictive control and optimization of networked microgrids

Kamal, Faria; Chowdhury, Badrul

doi:10.1016/j.ijepes.2021.107804

Cited by 37 publications

(17 citation statements)

References 79 publications

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“…The first three scenarios are when the HMPC is equipped with a time-invariant hydrogen storage model holding +20 %, 0 %, and − 20 % parametric errors in θ fc and θ els defined in Eq. (17). Finally, the fourth scenario is when the HMPC is endowed with the AOHS algorithm with a noise measurement of variance of 5 %.…”

Section: Comparison With the Time-invariant Modelmentioning

confidence: 99%

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Autonomous observer of hydrogen storage to enhance a model predictive control structure for building microgrids

Yamashita

Vechiu²,

Gaubert³

et al. 2022

Journal of Energy Storage

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Section: Comparison With the Time-invariant Modelmentioning

confidence: 99%

“…Among the existent EMS algorithms [13,14], model predictive control (MPC) has proved its robustness against environmental disturbances [15][16][17]. The capacity to consider prediction data and periodic optimisations over a sliding window are the main strengths of the MPC structure, making it highly appreciated for industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Autonomous observer of hydrogen storage to enhance a model predictive control structure for building microgrids

Yamashita

Vechiu²,

Gaubert³

et al. 2022

Journal of Energy Storage

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“…The effects of uncompensated dynamics of PCPC configuration have been studied in the design of the control structure. In [18], model predictive control schemes for energy management in networked microgrids have been studied. The voltage and frequency control performance has been embedded in the control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Affine projection‐like optimal control of UIPC in networked microgrids with controllable loads

Zolfaghari

Gharehpetian

2023

IET Control Theory & Appl

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The upcoming generation of power systems would be outlined as a system of systems in which many AC and DC microgrids are interconnected to exchange power in a controlled manner. In this work, a new platform for the interconnection of multiple microgrids using a unified interphase power controller (UIPC) is presented. The networked microgrids operate in an islanded mode where voltage control in DC microgrids, and voltage and frequency control in AC microgrids are demanding. Therefore, we develop a new structure for the UIPC according to a current‐controlled four‐leg power converter configuration which can satisfy voltage and frequency support at the AC side. Due to many power and voltage oscillations, UIPC control is challenging in this heterogeneous environment. Therefore, the UIPC is equipped with a new affine projection‐like optimal control (APLOC) strategy which enables the microgrids to safely exchange power when necessary. Further, each AC or DC microgrid contains a new controllable load model which is commanded by the UIPC according to the power management paradigm. Simulation results proved the sufficiency of the proposed networked microgrids model in both controlling the exchanged power among microgrids and improving power quality problems.

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“…To improve LVRT control of DFIGs in wind farms, methods that consider state prediction should be adopted. In recent years, model predictive control (MPC) has been widely used in the petroleum, chemical, and metallurgical fields, among others [13][14][15][16][17]. MPC performs limited rolling-horizon optimization based on a predictive model and the actual output.…”

Section: Introductionmentioning

confidence: 99%

Low‐voltage ride‐through handling in wind farm with doubly fed induction generators based on variable‐step model predictive control

Nie

Zhang

Liu

et al. 2023

IET Renewable Power Gen

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A severe voltage sag on the wind‐farm grid side with doubly fed induction generators (DFIGs) can induce a peak inrush current in the rotors and damage converters, resulting in wind‐turbine disconnection from the grid. To prevent this from happening, a variable‐step model predictive control (VS‐MPC) strategy is proposed for improving the wind turbines’ ability to operate without disconnecting themselves from the grid when a fault occurs. First, the predictive‐control state‐space model of a doubly fed wind farm is established according to its working principle. Second, model predictive control (MPC) is applied on the rotor side of the DFIG to realize the rapid tracking of the rotor current to the reference value during low‐voltage ride‐through (LVRT) of the DFIG. Finally, a variable‐step size algorithm is introduced into the MPC controller to change the step size continuously during the LVRT period. This increases the control accuracy, realizing rapid attenuation of each transient component, whereby the wind‐farm LVRT capability is enhanced. The proposed control strategy was simulated and verified using MATLAB/Simulink. The simulation results indicated that VS‐MPC can effectively handle LVRT, allowing the recovery of a wind farm that uses DFIGs and improving the performance of wind‐farm.

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Model predictive control and optimization of networked microgrids

Cited by 37 publications

References 79 publications

Autonomous observer of hydrogen storage to enhance a model predictive control structure for building microgrids

Autonomous observer of hydrogen storage to enhance a model predictive control structure for building microgrids

Affine projection‐like optimal control of UIPC in networked microgrids with controllable loads

Low‐voltage ride‐through handling in wind farm with doubly fed induction generators based on variable‐step model predictive control

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