Efficient Solar Panel Configuration for Indian Conditions Using Bio-Inspired Algorithms

Considering the geographic and climatic conditions of India, this paper discusses the configuration and design of solar panel systems that are both efficient and affordable. It examines methods like Ant Colony Optimization (ACO, MATLAB-based optimization framework and Genetic Algorithm (GA). The model operates with an average irradiance of 5 kWh/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>/day, and assumes an 18% solar panel efficiency. The key constraints include tilt angles ranging from 0°-45°, azimuth angles from -90° to 90°, and system sizes between 1 kW and 10 kW. Initial parameters are set within realistic ranges, with GA and ACO algorithms iterating through a series of configurations to identify optimal solutions. The findings underscore the importance of algorithmic optimization in renewable energy applications, demonstrating that a methodical approach to parameter selection can substantially enhance solar system performance and cost-efficiency, thus offering a scalable solution for energy stakeholders and policy makers in India and similar environments.