Development of Model Predictive Control of Hybrid Solar PV‐Wind for Economic Operation of Electrolyser System

The increasing penetration of renewable energy (RE) and hydrogen technologies is reshaping the operational requirements of modern decarbonised energy systems, necessitating advanced control strategies to coordinate renewable generation, hydrogen storage, and heterogeneous end‐use demands. This paper presents a mixed‐integer Model Predictive Control (MPC) framework for a grid‐connected hybrid microgrid integrating solar photovoltaics, wind turbines, and a proton exchange membrane elec-trolyser. The system simultaneously satisfies two distinct hydrogen demand profiles: continuous residential heating and discrete mobility demand via tube‐trailer refuelling, capturing temporal dynamics rarely addressed in prior studies. To manage discrete mobility demand beyond the prediction horizon, a critical‐time mechanism was introduced to ensures timely tube‐trailer dispatch while preventing premature grid electricity imports. In contrast to existing works that primarily adopt fixed hori-zons, this study systematically compares fixed and shrinking prediction horizons with 12‐h and 24‐h lengths, evaluating their impact on operational performance and mixed‐integer linear programming computational complexity. Simulation results show RE utilisation above 96% for heating and above 90% for mobility. Hydrogen production and grid electricity consumption vary by less than 0.1% across horizon configurations. Shrinking horizons cut computation time by up to 70%, demonstrating the MPC approach as a scalable, computationally efficient solution for integrating dual hydrogen demands.