GrCRA PCRTAM net based hybrid approach for intelligent control and optimal power management in renewable integrated power distribution systems

Power management in advanced power distribution systems integrated with Photovoltaic (PV) sources, batteries, and Super Capacitors (SCs) plays a vital role in ensuring stable and efficient energy flow. However, these systems often face drawbacks such as increased energy consumption due to inefficient control strategies, higher emissions from backup conventional sources during low PV output, and elevated operational costs from frequent battery cycling and system maintenance, despite efforts to improve efficiency and enhance renewable energy utilization. To overcome these drawbacks, this manuscript proposes an approach for optimal power management in a power distribution system with RES. The suggested method is the combination of both the Greater Cane Rat Algorithm (GrCRA) and Pre-Activated Convolution Residual and Triple Attention Mechanism Network (PCRTAM-Net), termed as the GrCRA-PCRTAM-Net approach. The primary aim of the suggested method is to reduce energy consumption, emissions, and operational cost while maximizing efficiency and renewable energy utilization in an advanced power distribution system. GrCRA optimizes the allocation and scheduling of power resources in advanced power distribution systems. PCRTAM-Net predicts future power demand and renewable energy generation patterns to support optimal power management. Flow Direction Algorithm-Convolutional Neural Network (FDA-CNN), Hippopotamus Optimization Algorithm (HOA), Particle Swarm Optimization (PSO), Spider Wasp Optimizer, and Multi-scale Hypergraph-based Feature Alignment Network (SWO-MHFAN), Golden Jackal Optimization-Progressive Conditional Generative Adversarial Network (GJO-PCGAN) are some of the existing techniques that are compared with the suggested method once it is implemented in MATLAB. An 18.7% overall energy reduction compared to the current methods has been achieved by GrCRA-PCRTAM-Net, which also attained an operational cost of 1505 cents, an emission level of 60.3 ppm, an efficiency of 99.1%, and a reduction in overall energy consumption. This further validates that the hybrid method effectively performed power flow optimization and stability enhancement in power distribution networks with renewable integration.