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

In power systems, the distribution network is pivotal for consumers because it is the final stage in the delivery of electricity from the generation plants to the end users. Reactive power demand from consumers can result in challenges like low power factor, diminished voltage, and heightened power losses. The primary challenge encountered when utilizing a radial system as a distribution network is the voltage drop, which leads to distortion in the voltage profile across the entire network. This research focuses on optimizing network performance by appropriately sizing and placing capacitors based on the Crow Search Algorithm (CSA). The results from this approach were compared with Particle Swarm Optimization (PSO), Artificial bee colony, Cultural Algorithm, Firefly Algorithm, Genetic Algorithm, Invasive Weed Optimization, and Teacher Learner Based Optimization methods illustrated in the study where CSA's effectiveness, demonstrated a notable 30.41% and 29.33% overall reduction in active and reactive power losses compared to the base case. The lowest bus voltage, notably at bus 18 which happens to be the longest in the IEEE 33 Radial Distribution Network (RDN) topology, improved from 0.8820 in the base case to 0.908 with CSA, showcasing a 32.9% enhancement in voltage deviation. This optimization led to a significant 3.841pu reduction in capacitor cost, highlighting CSA's efficacy in enhancing RDN performance and cost efficiency.

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