Minimizing power loss and enhancing voltage stability in distributed networks using an advanced metaheuristic approach: A case study on IEEE 33- and 69-bus networks

The combination of renewable distributed generation in the radial power distribution networks presents long-term difficulties associated with the loss of power quality, harmonic distortion, and fluctuating voltages, and is associated with high operational costs. In this article, a multi-objective optimization model is developed using the Aquila Optimizer Algorithm to find the optimal size and location of solar photovoltaic units, wind turbines, battery energy storage systems, and single-tuned filters in distribution networks. The framework simultaneously reduces the active power loss, overall operation cost, and harmonic distortion and maximizes the voltage stability. The suggested technique is tested on IEEE 33-bus and IEEE 69-bus benchmark systems and is compared with the results observed while incorporating Salp Swarm Algorithm and Water Cycle Algorithm under same scenario. The suggested method outperforms Salp Swarm Algorithm and Water Cycle Algorithm while achieving a power loss of 71.047 kW, terminal voltage of 0.9782 p.u., and THD of 3.2235% on IEEE 33-bus. Moreover, the suggested algorithm reduces power loss to 68.72 kW and THD to 3.0702%, compared to the system with Salp Swarm Algorithm. It is also evident that the incorporation of BESS and STF, the power loss reduces further to 8.4946 kW in IEEE 33-bus and 6.5273 kW in IEEE 69-bus, while the voltage profiles converge to unity. The operational costs are minimized to $228.51/hr and $258.32/hr, in IEEE 33-bus and IEEE 69-bus systems respectively. The findings prove that Aquila Optimizer Algorithm provides better convergence, robustness, and techno-economic performance for multi-objective DG planning in renewable-integrated distribution systems.