Intelligent Integration of Vehicle-to-Grid (V2G) and Vehicle-for-Grid (V4G) Systems: Leveraging Artificial Neural Networks (ANNs) for Smart Grid

Youness Hakam, Ahmed Gaga, Mohamed Tabaa () and Benachir Elhadadi
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Youness Hakam: Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal 23040, Morocco
Ahmed Gaga: Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal 23040, Morocco
Mohamed Tabaa: Multidisciplinary Laboratory of Research and Innovation (LPRI), Moroccan School of Engineering Sciences (EMSI), Casablanca 20250, Morocco
Benachir Elhadadi: Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal 23040, Morocco

Energies, 2024, vol. 17, issue 13, 1-19

Abstract: This paper presents a groundbreaking control strategy for a bidirectional battery charger that allows power to be injected into the smart grid while simultaneously compensating for the grid’s reactive power using an electric vehicle battery. An artificial neural network (ANN) controller is utilized for precise design to ensure optimal performance with minimal error. The ANN technique is applied to generate sinusoidal pulse width modulation (SPWM) for a bidirectional AC–DC inverter, with the entire algorithm simulated in MATLAB Simulink.The core innovation of this study is the creation of the ANN algorithm, which supports grid compensation using electric vehicle batteries, an approach termed “vehicle-for-grid”. Additionally, the paper details the PCB circuit design of the system controlled by the DSP F28379D board, which was tested on a three-phase motor. The total harmonic distortion (THD) of the proposed ANN algorithm is approximately 1.85 % , compared to the MPC algorithm’s THD of about 2.85 % . This indicates that the proposed algorithm is more effective in terms of the quality of the power injected into the grid. Furthermore, it demonstrates effective grid compensation, with the reactive power effectively neutralized to 0 KVAR in the vehicle-for-grid mode.

Keywords: artificial neural network; DC–AC inverter; DC–DC converter; MATLAB Simulink; vehicle-to-grid (V2G); vehicle-for-grid (V4G); DSPF28379D; PCB board (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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