Design of Hybrid renewable Energy & Storage System for Grid Connected Loads

Abstract

The following paper discusses one of the uses of artificial intelligence for the purpose of optimize the functioning of storing energy facilities with renewable energy sources, like solar and wind power. It begins by outlining the theoretical background of the renewable energy production process, technologies of energy storage with the emphasis on battery-based ones, and AI-based optimization strategies. This is followed by a study of how AI methods including machine learning and evolutionary algorithms may be applied to increase the competence, reliability, and economical feasibility of hybrid renewable energy systems. MATLAB is applied to simulate the practical situations of the solar photovoltaic panels, wind turbines, and battery storage systems. These simulations apply AI algorithms to optimise the way energy flows, how it is stored, and load balancing in dynamically changing environmental conditions. Case studies and simulated outcomes are provided that assess an efficacy and issues that come with the incorporation of AI. These results show that AI-based optimization helps to improve the concert of renewable Energy storage systems significantly and helps the transition to more ecologically friendly and low-carbon vitality infrastructure based on TFE.