Optimal Planning of Renewable Microgrids for Loss-Aware Integration of Distributed Energy Resources Using the Geese V-Formation Algorithm

This research introduces a unique optimization framework centered on the Geese V-Formation Algorithm to enhance the technical planning of distributed energy resources in renewable microgrid-oriented radial distribution systems. The proposed methodology addresses the optimal placement and sizing of photovoltaic panels, wind turbines, battery energy storage systems, and capacitor banks to provide comprehensive voltage support, minimize active power losses, and refine overall grid functionality. Drawing inspiration from the aerodynamic efficiency of migratory geese, the Geese V-Formation Algorithm integrates dynamic leader-follower coordination, adaptive role rotation, and cooperative information exchange mechanisms. These features allow the algorithm to effectively balance global exploration and local exploitation, making it uniquely suited to address the complex, nonlinear, and multi-objective nature of modern microgrid design. The effectiveness of this approach was evaluated through rigorous simulations on the IEEE-33 and IEEE-69 bus distribution systems utilizing the Python programming language. The empirical results indicate that the Geese V-Formation Algorithm achieves substantial power loss reductions, reaching 91.62% and 92.45%, respectively, when integrating solar and wind resources with energy storage and reactive power compensation. Furthermore, the optimized configurations significantly improved bus voltage profiles and enhanced substation power factors, confirming the technical effectiveness of the framework under the considered benchmark constraints. By providing a technical decision-support approach for engineers and utility planners, this framework facilitates the deployment of reliable, decentralized renewable energy systems that align with global energy transition objectives and promote sustainable infrastructure development.