Adaptive Neuro-Fuzzy Inference System for Optimizing Energy Flow in Grid-linked Solar and Battery Storage Systems

This paper presents a comprehensive study on implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for optimizing the energy flow in grid-linked photovoltaic (PV) and battery approaches. The increasing demand for renewable energy sources has led to the integration of solar power into the grid. However, the intermittent nature of solar energy and the variability in electricity demand pose significant challenges to energy management systems. To address these challenges, this study proposes an ANFIS-based approach that intelligently manages the energy flow between the PV system, battery storage, and the grid, ensuring optimal operation and increased efficiency. The proposed ANFIS model is designed to predict the optimal energy distribution strategy by considering various factors, including solar irradiance, electricity demand patterns, battery state of charge, and grid electricity prices. The system dynamically adjusts the charging and discharging of the battery and the supply of solar energy to the grid, aiming to minimize electricity costs and maximize the utilization of solar energy. Simulation results, conducted in MATLAB, demonstrate the effectiveness of the ANFIS model in enhancing the operational efficiency of grid-linked solar and battery storage arrangements. The model not only ensures a more stable and reliable energy supply but also contributes to significant cost savings and a reduction in carbon footprint. This study underscores the potential of adaptive neuro-fuzzy systems in advancing smart grid technologies and paves the way for more sustainable and efficient energy management solutions in the era of renewable energy integration.