Constant Power Load Stabilization in DC Microgrids Using Continuous-Time Model Predictive Control

Alidrissi, Youssef; Ouladsine, Radouane; Elmouatamid, Abdellatif; Errouissi, Rachid; Bakhouya, Mohamed

doi:10.3390/electronics11091481

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…If the MP obtained in [30] is analyzed, it can be seen that it is around 18% while in our research it is less than 5%, although different methodologies were used for the control, the effect of having a predictive control is better. In the research developed in [31], when injecting a disturbance in the MG, the general control takes around 0.05 s to stabilize the voltage, while in our research the time is around 0.01 s; although they are not exactly similar scenarios, the proposed methodology is superior in terms of robustness.…”

Section: Introductionmentioning

confidence: 69%

Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability

et al. 2022

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The paper presents the design and control strategy of an isolated DC microgrid, which is based on classical control techniques, predictive control and iterative algorithms. The design control parameters are maximum overshoot, settling time and voltage ripple. The strategy is designed to operate in two different modes, end-users minimum and maximum demand scenarios, and this is achieved through the incorporation of network dynamic loads. The control methodology developed allows to obtain a fast response of the design set points, and an efficient control for disturbance rejection. The simulation results obtained satisfy the proposed design guidelines by obtaining a maximum overshoot of 4.8%, settling time of 0.012 seconds and a voltage ripple of 0.1 percentage. The implemented system simulation was developed in Matlab-Simulink software.

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Section: Introductionmentioning

confidence: 69%

Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability

et al. 2022

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“…A longer interval can improve stability, reduce distortions, and boost efficiency. Additional calculations are required for each sampling interval, thereby increasing system complexity [38], [39]. The computational complexity of MPC-controlled power converters is reduced using different methods, such as multiparametric quadratic programming and linear programming.…”

Section: Microgrids Primary Control: Mpc-based Power Quality Enhancementmentioning

confidence: 99%

Model Predictive Control Strategies in Microgrids: A Concise Revisit

et al. 2022

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The world is rapidly integrating renewable energy resources into the existing grid systems. However, the unpredictable nature of renewables and uncertain load profiles cause issues such as poor power quality, lower system reliability, complex power management, battery degradation, high operating costs, and lower efficiency. Microgrids can help smart grid technology overcome several problems associated with renewable energy integration. Distant locations can obtain electricity without building extensive transmission infrastructure, cutting development costs, or transmission losses. The intermittent nature of renewable energy sources contributes to microgrid problems such as poor power quality, decreased reliability, and high operating costs. Model predictive control (MPC) is an effective method to address challenging industrial and scientific issues. Advancements in MPC that accept different system constraints have solved multiple concerns in uncertain microgrid systems. MPC applied to three hierarchal control layers in a microgrid resolves the problems of power quality, power sharing, energy management, and economic optimization. This study demonstrates that MPC microgrid control is suitable for low-cost operation, improved management, and reliable control. The shortcomings of recent model predictive control techniques for microgrids are reviewed, and future research directions for MPC microgrids are identified.INDEX TERMS Microgrids, renewable energy resources, model predictive control, power quality enhancement, energy management system, hybrid energy storage system, demand side management, demand response, distributed systems

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“…In [40], MPC has been used for dual active bridge DC-DC converters. Furthermore, in [41], MPC was implemented to stabilize DC microgrids with constant power loads.…”

Section: Control Of Pe Applicationsmentioning

confidence: 99%

Power and Energy Applications Based on Quantum Computing: The Possible Potentials of Grover’s Algorithm

et al. 2022

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In quantum computing, calculations are achieved using quantum mechanics. Typically, two main phenomena of quantum mechanics (i.e., superposition and entanglement) allow quantum computing to solve some problems more efficiently than classical algorithms. The most well-known advantage of quantum computing is the speedup of some of the calculations, which have been performed before by classical applications. Scientists and engineers are attempting to use quantum computing in different fields of science, e.g., drug discovery, chemistry, computer science, etc. However, there are few attempts to use quantum computing in power and energy applications. This paper tries to highlight this gap by discussing one of the most famous quantum computing algorithms (i.e., Grover’s algorithm) and discussing the potential applications of this algorithm in power and energy systems, which can serve as one of the starting points for using Grover’s algorithm in power and energy systems.

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Constant Power Load Stabilization in DC Microgrids Using Continuous-Time Model Predictive Control

Cited by 9 publications

References 39 publications

Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability

Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability

Model Predictive Control Strategies in Microgrids: A Concise Revisit

Power and Energy Applications Based on Quantum Computing: The Possible Potentials of Grover’s Algorithm

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