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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.
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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: M G MAHESH, mahi.leo5611@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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