Comparative analysis of optimization techniques for sizing and location of distributed generation in grid-connected microgrid

Efficient Distributed Generation (DG) sizing and allocation require advanced optimization techniques that facilitate real-time decision-making, improve power quality, reduce losses, and boost system performance while retaining cost efficiency. When searching in excessively complicated search spaces without parameter fine-tuning, conventional evolutionary optimization approaches frequently fail to identify global optima and become trapped in local optima. This research proposes three different optimization techniques, including Particle Swarm Optimization (PSO), the BAT algorithm, and Quantum Particle Swarm Optimization (QPSO), to determine the best locations and sizes of DG to lower power losses, improve voltage profiles, reduce pollutant emissions, and lower overall generation costs in the distribution network. Due to the increasing usage patterns of DGs, including wind turbines, solar units, and fuel cells, the suggested methodologies are implemented and assessed on the standard modified IEEE 33-bus test system. A comparison analysis was conducted using the proposed method after four DG cases were taken into consideration. According to the simulation results, PSO power loss reductions ranged from 43.32% to 67.24%, BAT power loss reductions from 51.78% to 71.57%, and QPSO power loss reductions from 52.21% to 74.27% Throughout the test system network, the DGs' voltage profiles improved. The convergence characteristics demonstrate that increasing the number of DG units significantly enhances loss minimization. The comparison of the results with those of other optimization strategies showed the effectiveness of the suggested QPSO technique in optimal placement and sizing of DG.