Multi-objective planning and optimal configuration of wind, solar, and energy storage in interconnected microgrid clusters using Vine Copula scenario generation and antlion optimization

The growing integration of renewable energy into modern power systems presents significant challenges for optimal distributed energy resource (DER) planning in interconnected microgrids due to the stochastic nature of wind and solar generation. To address this, we propose a robust multi-objective planning framework that combines Vine Copula-based spatiotemporal scenario generation with a Multi-Objective Antlion Optimizer (MOALO). The Vine Copula approach captures complex, non-Gaussian dependencies among renewable sources, generating realistic and correlated scenarios. The planning model aims to minimize both the annualized total cost and source–load mismatch, thereby enhancing economic efficiency and operational reliability. Simulation results on a three-microgrid system demonstrate that the proposed method achieves an annualized cost of 13,070 $, a source–load deviation of 3.75 × 10⁷ kW², and over 50 % renewable energy penetration, outperforming traditional methods such as MOPSO and NSGA-II. These findings validate the framework as an effective and resilient solution for cost-efficient microgrid planning under uncertainty.