Coordinated Planning of Collaborative Interconnected Microgrids Under Intermittent DERs and Dynamic Loads Using Geese V-Formation Algorithm (GVFA)

Interconnected microgrids (IMGs) offer opportunities to aggregate distributed energy resources (DERs) for loss reduction and voltage improvement, yet planning under daily variability remains challenging. This study presents a physics-aware planning and validation framework for five interconnected IEEE-33 microgrids integrating photovoltaic (PV), battery energy storage (BESS), one microgrid-specific Distributed Energy Resources (wind, hydro, biogas, or diesel), Electric Vehicle charging demand, and shunt capacitors (CAP). Snapshot optimization was performed using the Geese V-Formation Algorithm with a loss-focused objective, adequacy, and undervoltage penalties. The optimized configuration was validated over 24 hours using an AC backward–forward sweep under dynamic loads, weather-based Distributed Energy Resources generation, and Battery Energy Storage Systems state-of-charge constraints. The final GVFA planning simulation, implemented in Python, demonstrated feeder loss reductions of 50–70% and minimum voltage improvements between 0.956 and 0.966 p.u., with higher values during hourly operation. Three microgrids consistently acted as net-exporters (island-capable), while two remained net-importers, leveraging tie-line diversity. Methodologically, Geese V-Formation Algorithm’s leader–follower dynamics, neighbor-local coupling, and stochastic rounding efficiently traversed a mixed-integer, nonconvex search space. Systemically, the collaborative interconnected approach delivered system-wide benefits beyond localized optimization. The framework provides actionable recommendations for Distributed Energy Resources siting and sizing to enhance loss reduction, voltage stability, and operational flexibility under realistic intermittent conditions.