Solar power fluctuation smoothing through battery energy storage system using AVOA-SAGAN approach

Power fluctuations induced by photovoltaic hinder large-scale solar power from entering the grid because they create several instabilities like frequency deviations, voltage variations, and reduced output power quality. A Battery Energy Storage System (BESS) combined with photovoltaic power smoothing is proposed as a solution to these problems. This manuscript presents a hybrid approach for solar power fluctuation smoothing BESS. The proposed method is the combined implementation of both the African Vultures Optimization Algorithm (AVOA) and Self-Attention Generative Adversarial Networks (SAGAN), which is termed as AVOA-SAGAN technique. The primary objectives of the proposed method are to minimize errors and achieve optimal performance. The AVOA optimizes the battery's discharging and charging procedures. The SAGAN is used to predict solar power fluctuation. The proposed technique is compared to other existing techniques on the MATLAB platform. The proposed method demonstrates improved outcomes in all existing methods such as Humble-Bee Optimization, Particle Swarm Optimization and Wild Horse Optimization. The existing methods attain errors of 0.4, 0.3 and 0.2 and the proposed method attains a lower error of 0.1. The proposed method also has a low settling time of 172 ms. The outcomes displays that compared to the existing approaches, the proposed method performs better.