A HYBRID CAPSA-GR-RNN BASED ANALYSIS OF HIGH RENEWABLE ENERGY PENETRATION UNDER THE ELECTRICITY ACT, 2003 FOR SMART GRID SYSTEMS

Povzetek

A Smart Grid System is an advanced electrical grid employing digital communication to enhance electricity flow control, boosting efficiency, reliability, and sustainability of energy distribution. Renewable energy sources, like sunlight, wind, water, and biomass, offer clean and sustainable energy but suffer from intermittency due to weather variations and can lead to land use conflicts as hefty installations may compete with agriculture or natural environments. The Capuchin Search Algorithm (CapSA) and Global-Context Residual Recurrent Neural Networks (GR-RNN) work together to create the CapSA-GR-RNN Approach, a hybrid method for smart grid systems with high penetration of renewable energy sources (RES). The main goal of the proposed technique is to reduce operational cost, pollution emission, and maximize availability by using RES. CapSA is used to optimize the performance of smart microgrids, and GR-RNN is used to predict the behavior of RES, such as the Probability Density Function (PDF) and Cumulative Density Function (CDF).This method has been implemented on the MATLAB platform. The proposed method is compared with existing methods such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer and Sparrow Search Algorithm,.  In the proposed approach, the cost is $0.93, whereas the existing methods incur costs of $1.08, $0.96, and $0.98. This indicates that the proposed method has a potential cost saving capability linked to the existing methods.