Adaptive genetic algorithm and enhanced particle swarm optimization for static voltage stability enhancement in radial distribution systems with distributed generation integration

Voltage instability in the electrical power distribution system is becoming a major problem. The sharp increase in power consumption throughout electrical distribution networks is the primary cause of this instability. This study offers a systematic solution by introducing allocation and sizing of distributed generation (DG) in order to enhance voltage stability, lower power losses and raise the voltage profile. Genetic algorithm (GA) and particle swarm optimization (PSO) are two optimization methods that were developed and tested on the IEEE 33-bus and the actual Bahir Dar distribution system in Ethiopia to assess their applicability and effectiveness. Comparative analyses were conducted against existing techniques to assess the performance of the developed GA and PSO-based approaches. The results demonstrate that the integration of DG using the proposed optimization methods led to substantial improvements in the loading factor of the distribution systems. Specifically, the 35-bus case study achieved an 11.553% and 17.529% increase in loading factor using GA and PSO, respectively. Similarly, the 53-bus system gained loading factor improvements of 5.538 and 6.153% with GA and PSO. Notably, the PSO algorithm outperformed GA in terms of voltage stability index (VSI), voltage profile enhancement, and loss minimization through DG integration. Distributed generation placement improves stability and reduces power losses in distribution systems Particle swarm optimization outperforms genetic algorithm in enhancing voltage stability and efficiency Case study on Bahir Dar network validates effectiveness of solar DG for reliable power distribution