Swarm intelligence and bio-inspired algorithms based active power loss reduction in microgrids using optimal placement and sizing of renewable distributed generators

Renewable energy sources now play a crucial role in meeting the world's growing energy needs sustainably. These sources, such as solar and wind energy, help reduce carbon emissions and protect the environment, making them an essential component of future energy strategies. In this paper, one of swarms’ intelligences algorithms called particle swarm optimization (PSO) and one of the most general bio-inspired metaheuristic methods called BAT algorithm are applied to determine the optimal placement and sizing of renewable distributed generators (OPSRDG) in distributed network. The focus is on enhancing energy efficiency and reduced dependency on the main grid by reducing power losses, improved voltage profile, enhanced reliability, stability enhancing the overall system stability and performance of the grid. Compared with recently published results, the obtained results are promising and demonstrate the effectiveness and robustness of the proposed approach to solve OPSRDG problems. When the DG units are optimally integrated into the system, the power loss reductions ranged from 48.23% to 71.57%. This represents a significant improvement over conventional methods, making it a promising contribution to society and academia.