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. 2.4. Positioning of the Present Study and Its Contributions This study addresses the above gap by proposing a Geese V-Formation Algorithm (GVFA)-based framework for technical DER placement and sizing in renewable microgrid-oriented radial distribution systems. Inspired by the efficiency of V-shaped flight formation, GVFA employs dynamic leader–follower coordination, adaptive leader rotation, and cooperative information sharing to improve search efficiency and maintain a balanced exploration–exploitation trade-off in nonlinear, multi-constraint nonlinear design spaces. Unlike many prior approaches that emphasize either siting/sizing or EMS in isolation, the proposed framework jointly optimizes the placement of PV, WT, BESS, and shunt capacitors to deliver coordinated improvements in voltage support, loss reduction, and power-factor enhancement. The framework is implemented in Python 3.12 within the PyCharm Community 2026 integrated development environment (IDE) and validated on IEEE-33 and IEEE-69 test systems.
Applied Sciences, Vol. 16, Pages 5797: Optimal Planning of Renewable Microgrids for Loss-Aware Integration of Distributed Energy Resources Using the Geese V-Formation Algorithm
