Application of data reconciliation methods for performance monitoring of power-to-gas plants

Power-to-gas plants have recently gained more attraction as these storage systems can be deployed in the European energy grid to ease the integration of renewable energy sources. Most studies in this field address several aspects, from their technical performances to their economic costs and environmental impacts. However, these novel systems are inherently subject to large variations of energy supplies and operating costs, but few works, if none, deal with their real-time monitoring. Deriving consistent and satisfactory results of measurements data is essential and possible only by application of data validation and reconciliation (DVR) methods. In the present study, conventional and advanced DVR techniques were combined with process models and cross-correlation techniques to reconcile the measurement sets, and their outcomes were compared for two case studies. This approach aims at identifying the most relevant measurements and providing a better understanding of the system behaviour under different loads. The results underlined the impact of the number, type and location of redundant measurements on the quality of the reconciled values. For both plants, the measurement uncertainties would be greatly reduced by a better monitoring of the flow and temperature sensors at the inlet and outlet of the methanation reactors/processes. In addition, a net improvement of the model robustness was obtained with the advanced DVR approach, at the expense of a greater resolution time. This computational burden was not deemed critical if measurement datasets were assessed with a timestep in the order of minutes to hours.