Cost-effective and sustainable operation of microgrids using Improved Whale Optimization Algorithm

The global transition to sustainable energy demands efficient integration of renewable resources and resilient operation of microgrids (MGs). This study aims to develop a cost-effective and sustainable Energy Management System (EMS) for MGs operating in both grid-connected and islanded modes. The inherent variability of renewable generation and fluctuating grid prices pose significant challenges to maintaining supply-demand balance. To address this, the proposed EMS employs an Improved Whale Optimization Algorithm (IWOA), incorporating a nonlinear swimming parameter and Lévy flight mechanism to prevent premature convergence. Simulation results on a benchmark low-voltage MG reveal that IWOA achieves a 39.66% reduction in operational costs compared to standard algorithms, while maintaining competitive runtime of 4.2 min. Furthermore, a dynamic energy trading strategy is integrated to optimize real-time interactions with the main grid. The findings validate the proposed framework as a robust solution for enhancing the economic and environmental performance of modern power systems.