Hybrid model predictive control of renewable microgrids and seasonal hydrogen storage

Optimal energy management of microgrids enables efficient integration of renewable energies by considering all system flexibilities. For systems with significant seasonal imbalance between energy production and demand, it may be necessary to integrate seasonal storage in order to achieve fully decarbonized operation. This paper develops a novel model predictive control strategy for a renewable microgrid with seasonal hydrogen storage. The strategy relies on data-based prediction of the energy production and consumption of an industrial power plant and finds optimized energy flows using a digital twin optimizer. To enable seasonal operation, incentives for long-term energy shifts are provided by assigning a cost value to the storage charge and adding it to the optimization target function. A hybrid control scheme based on rule-based heuristics compensates for imperfect predictions. With only 6% oversizing compared to the optimal system layout, the strategy manages to deliver enough energy to meet all demand while achieving balanced hydrogen production and consumption throughout the year.