Optimizing solar panel orientation for enhanced power output in cloudy conditions: a case study on solar energy supply in a water treatment plant

Abstract

This paper presents a study on optimizing the performance of a photovoltaic solar generator used to power a water treatment plant in a remote area. The research aims to evaluate the system's efficiency and focuses on determining the optimal orientation of the solar panel during periods of cloudy weather, which significantly impact sunlight availability and ozone production. To achieve this, two optimization algorithms, namely the Grey Wolf Algorithm (GWO) and the Crayfish Optimization Algorithm (COA), are employed to optimize the power output. The study is conducted in Algeria, where prevalent autumn cloud cover poses challenges for solar panel operation. By addressing these challenges and designing a high-performance photovoltaic-powered water treatment plant, the research contributes to sustainable water treatment technologies and offers promising solutions for underserved communities in need of water pumping and treatment systems. The findings highlight the importance of optimizing solar panel orientation in cloudy conditions and showcase the effectiveness of the GWO and COA algorithms in maximizing power output. By leveraging these optimization techniques, the proposed approach enhances the performance of the solar generator, ensuring efficient power production even during periods of reduced sunlight. This research not only addresses the specific case of a water treatment plant in a remote area but also has broader implications for the renewable energy sector. The study demonstrates the potential of utilizing solar energy in powering critical infrastructure, such as water treatment facilities, in areas where access to reliable electricity is limited. By fostering the adoption of sustainable energy solutions, this research contributes to the goal of achieving long-term environmental and social sustainability.