An Effective Integrated Optimal Day-ahead and Real-time Power Scheduling Approach for Hydrogen-based Microgrid

The increasing penetration of renewable energy sources in power systems poses significant challenges for maintaining grid reliability, mainly due to the variability and uncertainty of solar and demand profiles. Microgrids, equipped with diverse storage technologies, have emerged as a promising solution to address these issues.This paper proposes an integrated day-ahead and real-time power scheduling approach for grid-connected microgrids equipped with both conventional and hydrogen-based ESSs. While existing strategies often address day-ahead and real-time scheduling separately or rely on a single storage technology, this work introduces a unified framework that exploits the complementary characteristics of batteries and hydrogen systems. The proposed approach is based on a novel two-stage stochastic optimization model, embedded within a hierarchical optimization framework to address these two intertwined problems efficiently. For the day-ahead scheduling, a two-stage stochastic programming energy management model is solved to optimize the microgrid schedule based on forecasted load demand and PV production profiles. Building upon the day-ahead schedule, another optimization model is solved, which addresses real-time power imbalances caused by deviations in actual PV production and load demand power profiles with respect to the forecasted ones, with the aim of minimizing operational disruptions. Simulation results demonstrate the validity of the proposed approach, achieving both cost reductions and minimal power imbalances. By dynamically adjusting energy flows and using both conventional batteries and hydrogen systems, the proposed approach ensures improved reliability, reduced operational costs, and enhanced integration of RES in microgrids. These findings highlight the potential of the proposed hierarchical framework to support the large-scale deployment of RES while ensuring resilient and cost-effective microgrid operations.