Integrated Renewable Energy Storage System with Enhanced Self-Adaptive Differential Evolution Algorithm on Profit Maximization

In this work addresses the surge in greenhouse gas emissions and fuel costs resulting from heightened energy demand, especially in developing nations. To counter these challenges, the focus is on optimizing renewable energy sources, which, though advantageous, are weather-dependent and require intricate management. The study introduces the Enhanced Self-Adaptive Differential Evolution (SADE) algorithm, encompassing solar, battery, and thermal sources, to maximize profitability. Real-time (RT) load profiles are used for performance analysis, comparing the proposed algorithm with existing techniques like PSO, Differential Evolution (DE) algorithm, and SADE algorithm. Improved energy storage technologies complement the increased utilization of renewable energy, enabling electricity storage during off-peak hours and release during peak demand, providing promising solutions for the energy industry.