A day ahead scheduling model of a smart hydrogen-based microgrid taking into account PV production and electrical load demand forecasting errors

• A MILP-based model schedules hybrid microgrid with forecast uncertainty • PV and load errors modeled with a probabilistic approach for robust scheduling • Hydrogen and batteries combined for short- and long-term energy storage • Enhances energy resilience, self-sufficiency, and system cost-effectiveness . Microgrids are increasingly recognized as efficient and sustainable solutions for modern energy management, especially in scenarios requiring high resilience and self-sufficiency. The intermittent nature of renewable energy sources presents significant challenges in balancing energy supply and demand. The integration of both short- and long-term energy storage systems represents a viable solution to these issues. Among the various long-term storage systems, the strategic importance of hydrogen as a long-term storage solution within microgrids is well recognized, enabling greater integration of renewables, enhancing energy autonomy, and contributing to the decarbonization of energy systems. To ensure the proper operation of such systems, scheduling models that optimally coordinate these resources become necessary. However, it is challenging to find scheduling models in the literature that incorporate photovoltaic production and electrical load consumption forecast errors. This paper aims to address this lack in the literature, introducing a probabilistic approach to model photovoltaic production and electrical load consumption forecast errors to integrate into the scheduling model. For this purpose, a hydrogen-based microgrid modular configuration is proposed, integrating both a battery and a hydrogen-based storage system. On this microgrid configuration, a day-ahead (short-term) scheduling Mixed-Integer Linear Programming model is developed to schedule power flow among the different microgrid components minimising the operational costs and considering the impact of production and consumption forecast errors on system reliability, ensuring a robust energy management. This study is part of the "SmartHydroGrid" project, which aims to develop innovative digital solutions to optimize energy management of hybrid energy systems, multi-energy and multi-sector. Simulation results show an improvement from 10% to 20% in renewable energy utilization. The system achieves 80.1% self-sufficiency. Moreover, the probabilistic approach ensures robust scheduling decisions with less than 2% deviation from optimal costs across uncertainty scenarios. The primary objective of the project is to create a digital twin of a hybrid smart grid, enabling optimized and interconnected management of energy resources. The proposed model is validated using the data of a real-life application, the SmartHydroGrid deployed at the Techfem S.p.A. company site, in Italy.