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The article explores the integration of photovoltaic (PV) and wind energy systems, electric vehicle (EV) charging systems, and a hybrid DC microgrid within a smart university setting. The aim is to meet the energy demands of various loads by considering the power supplied by PV panels, wind turbines, and a battery storage system (BSS). To achieve this, an intelligent control model has been proposed to manage the BSS, ensuring a stable energy supply for all components within the hybrid microgrid and enabling voltage control. The research presents an exhaustive study of a microgrid energy management system (EMS), which integrates AC/DC loads, Li-ion batteries, backup electrical networks, and renewable energy sources like solar panels and wind turbines. The Maximum Power Point Tracking (MPPT) mode of the EMS is used to maximize the use of energy from renewable sources. Furthermore, by using artificial neural network controllers (ANNCs) for optimal regulation of battery charging and discharging, efficient management of the stored energy is accomplished. Maintaining power balance inside the DC microgrid is the primary aim of this system, which also provides resilient and adaptable control that provides for a variety of scenarios and alterations. Results from MATLAB simulations are used to verify the viability and efficacy of the suggested method and technique in microgrid control under various operating situations.

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Energy Supply Control for a Hybrid Microgrid Using an Artificial Neural Network with Integrating of an EV Charging Station: A Case Study of Smart University in a DC Hybrid Microgrid

  • 章节

  • 发表日期：

    2025年

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  • Elmehdi Nasri,
  • Tarik Jarou,
  • Abderrahmane Elkachani

摘要

The article explores the integration of photovoltaic (PV) and wind energy systems, electric vehicle (EV) charging systems, and a hybrid DC microgrid within a smart university setting. The aim is to meet the energy demands of various loads by considering the power supplied by PV panels, wind turbines, and a battery storage system (BSS). To achieve this, an intelligent control model has been proposed to manage the BSS, ensuring a stable energy supply for all components within the hybrid microgrid and enabling voltage control. The research presents an exhaustive study of a microgrid energy management system (EMS), which integrates AC/DC loads, Li-ion batteries, backup electrical networks, and renewable energy sources like solar panels and wind turbines. The Maximum Power Point Tracking (MPPT) mode of the EMS is used to maximize the use of energy from renewable sources. Furthermore, by using artificial neural network controllers (ANNCs) for optimal regulation of battery charging and discharging, efficient management of the stored energy is accomplished. Maintaining power balance inside the DC microgrid is the primary aim of this system, which also provides resilient and adaptable control that provides for a variety of scenarios and alterations. Results from MATLAB simulations are used to verify the viability and efficacy of the suggested method and technique in microgrid control under various operating situations.