A multi-stage hierarchical microgrid planning approach for maximizing the hosting capacity of renewable energy

The integration of high levels of renewable energy into microgrids presents significant challenges due to the inherent variability of resources like solar and wind, coupled with uncertainties in demand and market dynamics. To address this, we propose a multi-stage hierarchical optimization framework that systematically coordinates long-term infrastructure investments with short-term operational strategies to maximize renewable hosting capacity. The framework employs a three-level structure: (1) a master problem optimizing asset capacities (photovoltaics, wind turbines, electrolyzers, fuel cells, hydrogen/battery storage, electric vehicles, and demand response); (2) scenario-based subproblems for operational dispatch under uncertainties; and (3) iterative coordination via Benders decomposition to align planning with real-time performance. A risk-aware multi-objective function balances investment costs, operational expenses, and Conditional Value-at-Risk (CVaR) at a 95% confidence level to mitigate extreme financial risks. Simulation results demonstrate that the framework increases renewable hosting capacity by 15–20% compared to conventional methods while reducing operational costs by 12% and enhancing resilience against adverse scenarios. By integrating diverse energy assets, risk management, and computational efficiency through hierarchical decomposition, this approach provides a robust tool for designing sustainable microgrids with high renewable penetration.