renewable energy integration and urban air quality improvement through model predictive microgrid control

This paper presents a model predictive control (mpc)-based optimization framework for enhancing the performance and sustainability of renewable-energy microgrids. the proposed system dynamically integrates photovoltaic and wind power sources with energy storage to minimize losses, improve voltage stability, and reduce fossil fuel dependency. by intelligently predicting demand and generation profiles, the mpc controller ensures optimal power flow while maintaining air quality through reduced emissions. the study highlights that sustainable microgrid management not only improves energy efficiency but also supports low-carbon urban development, aligning with the environmental goals of green and smart cities.