Voltage enhancement and loss minimization in a radial network through optimal capacitor sizing and placement based on Crow Search Algorithm

Abstract

In power systems, the distribution network is crucial as it serves as the final stage in delivering energy from generation plants to end users. However, reactive power demand from consumers can create challenges such as low power factor, voltage drops, and increased power losses depending on the distribution system used. This study aims to enhance the performance of radial distribution networks (RDNs) by optimizing capacitor placement and sizing based on the Crow Search Algorithm (CSA). Additionally, the study addresses the Optimal Power Flow (OPF) within the network by employing the Backward/Forward Sweep (BFS) method. The study accesses CSA’s effectiveness compared to other metaheuristic optimization techniques through simulation. The simulation results showed that the Voltage Stability Index improved from 0.61 for base case to 0.68 pu with CSA, compared to 0.66 for Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Teacher Learner Based Optimization (TLBO and 0.67 for Invasive Weed Optimization (IWO). CSA also achieved a 30.41 % reduction in active power losses, outperforming PSO, ABC, GA, and TLBO having 26.61 %, and IWO at 24.92 %. Additionally, application of CSA led to a 29.33 % reduction in reactive power losses, compared to 21.91 % for PSO, ABC, GA, and TLBO, 18.2 % for IWO. In the simulated IEEE 33 bus Radial Distribution Network (RDN) topology, the lowest bus voltage at bus 18 increased from 0.8820 in the base case to 0.9080 with CSA, indicating a 32.9 % improvement in voltage deviation. Furthermore, the optimization resulted in a significant reduction of 3.841 pu in capacitor cost, demonstrating CSA’s efficiency in both technical and economic aspects. CSA not only showed favorable results in minimizing power losses and improving voltage profiles but also provided substantial cost savings in capacitor implementation, making it a robust and cost-effective solution for enhancing RDN performance.