AI Algorithms for Advanced Energy Management Strategies of Hybrid Solar Systems

Abstract

This paper presents a comprehensive energy management mechanism for hybrid solar systems from different aspects of solar energy generation, battery storage, and grid coupling. The proposed system operates through on advanced modes—Hybrid, Smart, and Power Conditioning Unit (PCU)—which are used for power distribution in real-time for the uninterrupted supply of energy. Utilizing Maximum Power Point Tracking (MPPT) controllers, inverters, and smart algorithms such as Reinforcement Learning (RL) and Fuzzy Logic, the system reaches high efficiency, adaptability, and sustainability. The hybrid mode here helps to distribute energy flows among local solar power, batteries, and the grid, while the smart mode adjusts resource allocation from the grid dynamically based on real-time weather and environmental conditions to optimize the use of renewable energy as well as maintain the battery health. PCU mode prioritizes avoiding grid outages and low solar with very low availability & resource preservation. This research illuminates the pivotal function of intelligent hybrid systems in enhancing sustainable energy solutions, thereby facilitating the generalized employment of renewable energy.