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<p>The rapid integration of solar and wind energy into power grids is a significant stride toward a more sustainable energy future. However, their intermittent and unpredictable behaviour presents considerable obstacles to the efficient planning and operation of power grids. This study proposes a hybrid renewable energy system with enhanced performance achieved through two key components: an artificial neural network (ANN)-based maximum power point tracking (MPPT) controller for the photovoltaic (PV) system, and a Fuzzy logic control system for optimized energy management (OEM). This innovative OEM aims to optimizes energy distribution, decrease reliance on the grid, and enhance the overall efficiency of the system under study, leading to lower costs and environmental advantages. Our research reveals that implementing the ANN algorithm for MPPT yields a notable 5% improvement in efficiency over traditional methods like P&amp;O. Its adaptability and accuracy enable dynamic adjustments to real-time environmental conditions, reducing energy losses and improving solar energy conversion. Furthermore, our findings highlight the efficacy of the proposed OEM for hybrid systems in meeting electrical load requirements, significantly reducing grid dependency and enhancing the overall efficiency of the system, leading to lower costs and environmental advantages. Our research demonstrates the effectiveness of this hybrid system in meeting electrical load demands, even under variable renewable energy conditions.</p>

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Enhancing efficiency and sustainability: a combined approach of ANN-based MPPT and fuzzy logic EMS for grid-connected PV systems

  • 期刊论文

  • 发表日期：

    2025年1月16日

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  • Aymen Lachheb,
  • Jaouher Chrouta,
  • Noureddine Akoubi,
  • Jamel ben Salem

摘要

The rapid integration of solar and wind energy into power grids is a significant stride toward a more sustainable energy future. However, their intermittent and unpredictable behaviour presents considerable obstacles to the efficient planning and operation of power grids. This study proposes a hybrid renewable energy system with enhanced performance achieved through two key components: an artificial neural network (ANN)-based maximum power point tracking (MPPT) controller for the photovoltaic (PV) system, and a Fuzzy logic control system for optimized energy management (OEM). This innovative OEM aims to optimizes energy distribution, decrease reliance on the grid, and enhance the overall efficiency of the system under study, leading to lower costs and environmental advantages. Our research reveals that implementing the ANN algorithm for MPPT yields a notable 5% improvement in efficiency over traditional methods like P&O. Its adaptability and accuracy enable dynamic adjustments to real-time environmental conditions, reducing energy losses and improving solar energy conversion. Furthermore, our findings highlight the efficacy of the proposed OEM for hybrid systems in meeting electrical load requirements, significantly reducing grid dependency and enhancing the overall efficiency of the system, leading to lower costs and environmental advantages. Our research demonstrates the effectiveness of this hybrid system in meeting electrical load demands, even under variable renewable energy conditions.