Cost‐Effective Optimal Integration of Renewable Energy and Waste‐to‐Energy Technologies

The growing demand for reliable and clean energy in remote regions necessitates innovative solutions that balance economic viability and sustainability. This study proposes a novel cost‐effective optimization framework for a hybrid microgrid integrating photovoltaic (PV) panels, wind turbines (WT), waste‐to‐energy (WtE) systems based on FastOx gasification, diesel generators (DG), and battery energy storage. The primary objective is to minimize the levelized cost of energy (LCoE) and greenhouse gas emissions while ensuring energy reliability. A hybrid optimization approach utilizing particle swarm optimization (PSO) and grey wolf optimization (GWO) is developed to determine the optimal sizing of system components. The methodology is applied to a real‐world case study in Halishahar, Bangladesh, incorporating local meteorological data and municipal solid waste profiles. Simulation results show that GWO achieves the lowest LCoE of 0.221 $/kWh, outperforming PSO (0.223 $/kWh) and HOMER software (0.468 $/kWh). Additionally, the integration of WtE reduces emissions and enhances energy diversity. Comparative analyses validate the convergence and effectiveness of the proposed method, demonstrating its superiority in terms of techno‐economic and environmental performance compared to existing approaches. This research provides a practical pathway for the sustainable deployment of microgrids in developing countries.