Hybrid Solar Energy System with AI-Based Predictive

Background: Energy Management Techniques play an important role in innovative microgrid systems primarily dependent on Sustainable Energy Sources (SES) power generation. The irregular weather conditions, low power backup, managing the combination of ESS, and ensuring the distribution generation's reliability and low power quality. Methods: To solve these limitations, this study proposes a novel hybrid optimization technique that combines the Symbiotic Search Algorithm (SSA) and Whale Optimization Algorithm (WOA) for managing Renewable Energy (RE) sources. The proposed system integrates hybrid wind Photovoltaic and Wind energy systems with an advanced Hybrid Energy Storage System (HESS) that includes Battery Energy Storage (BES) and SC technology in a Microgrid (MG). Renewable energy sources ensure power balance by addressing variations in load demand and fluctuations in renewable energy generation. The output testing and validation are facilitated through control parameters; the SSA-WOA-based microgrid offers an experimental framework for bright grid power quality.