Model-based optimisation of a hydrogen system for economic and grid supporting operation with regulatory constraints

This study presents two multi-objective optimisation methods for hydrogen-based energy storage systems (HBESS) that integrate economic performance, grid support in terms of alleviating grid congestion, and regulatory constraints defined by the German Renewable Energy Act (EEG) within the optimisation process. The first method employs model predictive control (MPC) for operational management, aiming to maximise either revenue or grid support. The results indicate that during congestion periods, 91% of the power output contributed to alleviating the congestion, demonstrating that HBESS are well-suited for providing grid support. However, while maximising grid support improves component utilisation, it negatively impacts economic performance. Furthermore, the analysis shows that the EEG might not adequately promote grid supporting operations, emphasising the need for regulatory adjustments, including potential modifications to the EEG during congestion periods and a clear compensation framework for surplus electricity redirected to storage. The second optimisation method focuses on the optimal sizing of HBESS components. Findings suggest that profitability of the HBESS is achieved when capital expenditure and operating expenses are both reduced by 50%. Therefore, despite EEG subsidies, investing solely in a wind turbine proves to be more cost-effective than hydrogen production for the specified use case, further emphasising the need for regulatory revisions.