Efficient Power Management Strategy of Electric Vehicles Based Hybrid Renewable Energy

Mohamed, Naoui; Aymen, Flah; Ali, Ziad M.; Zobaa, Ahmed F.; Aleem, Shady H. E. Abdel

doi:10.3390/su13137351

Cited by 40 publications

(19 citation statements)

References 41 publications

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“…where K sys is the frequency characteristic constant of the system. Because there is a delay between frequency changes and power deviation, the transfer function for system frequency changes in terms of per unit frequency deviation can be determined by Equation (3) [29]:…”

Section: Power and System Frequency Deviationmentioning

confidence: 99%

“…In [29], the authors conducted research for the control of island microgrids to reduce the frequency and power fluctuations and in [30] for intelligent frequency control for an AC microgrid or a combination of the particle swarm algorithm and fuzzy logic. In [31], the authors focused on the stability and control of the microgrid, which included a combination of microturbines, fuel cells and electrolyzers and was self-regulated by a fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV

et al. 2021

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This article studied the load frequency control (LFC) of a multi-source microgrid with the presence of renewable energy sources. To maintain a sustainable power supply, the frequency of the system must be kept constant. A Proportional–Integral–Derivative (PID) controller is presented as a secondary controller to control the frequency of the microgrid in island mode, and the integral of squared time multiplied by error squared (ISTES) is used as a performance index. The use of the Craziness-Based Particle Swarm Optimization (CRPSO), which is an improved version of Particle Swarm Optimization (PSO), improves the convergence speed in optimizing the nonlinear problem of load and frequency controller design. The test microgrid is composed of the load and distributed generation units such as diesel generators, photovoltaics and wind turbines. The proposed controller provided the desired response to adjusting the microgrid frequency, achieving the final response after a short time and making it more stable and less oscillatory compared with the conventional system.

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Section: Power and System Frequency Deviationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV

et al. 2021

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“…Therefore, the design of an optimized PV system becomes difficult. Consequently, the development of efficient techniques to overcome these problems is of paramount importance [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems

et al. 2022

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Increasing energy needs, pollution of nature, and eventual depletion of resources have prompted humanity to obtain new technologies and produce energy using clean sources and renewables. In this paper, we design an advanced method to improve the performance of a sliding mode controller combined with control theory for a photovoltaic system. Specifically, we decouple the controlled output of the system from any perturbation source and assess the effectiveness of the results in terms of solution quality, closed-loop control stability, and dynamical convergence of the state variables. This study focuses on the climatic conditions that may affect the behavior of a solar energy plant to supply a motor with the highest possible efficiency and nominal operating conditions. The designed method enables us to obtain an optimal performance by means of advanced control techniques and a slime mould stochastic optimization algorithm. The efficiency and performance of this method are examined based on a benchmark model of a photovoltaic system via numerical analysis and simulation.

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“…Moreover, electricity generation from this resource is quite viable for a variety of uses. In particular, the rapid development of solar energy instruments gives a complete kit of tools that can be directly applied into the field of Electric Vehicles (EV) [3,4]. Several studies have suggested that photovoltaic cells can be used to cover EVs' surfaces to store a significant amount of electricity in the storage system [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

et al. 2021

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This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified.

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Efficient Power Management Strategy of Electric Vehicles Based Hybrid Renewable Energy

Cited by 40 publications

References 41 publications

Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV

Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV

A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems

A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

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