Energy Storage System Optimization for Renewable Energy Using Real Time Date to Maximize Self-Consumption and Efficient Energy Management

The increasing global demand for sustainable energy sources has caused significant advancements in solar energy technologies. Solar (PV) systems have emerged as a solution against climate change, but their unstable nature poses challenges for efficient utilization. This study presents a Model system designed to medicate these challenges by optimizing the operation of solar energy systems. The system for solar energy optimization leverages real-time data acquisition from various sources, including solar irradiance forecasts, weather conditions, and load demand. It employs a predictive control algorithm that dynamically adjusts the operating parameters of solar PV systems, such as tilt angle, orientation, and energy storage utilization, to maximize energy production and grid integration efficiency while ensuring system stability. Key features of this model are Data integration, Energy management, Grid integration and Control Optimization. In this system the energy management optimization results in cost savings which is 2673Tk for that month. This also results in CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> reduction of 454.4 kg. This helps reduce the effect of climate change which is the main goal of PV power installation. Also, the battery management system ensures the long life of the system.