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This paper presents the development, implementation and validation of a simplified dynamic modeling approach to describe SOFC/GT hybrid systems in three real emulator test-rigs installed at University of Genoa (UNIGE, Italy), German Aerospace Center (DLR, Germany) and National Energy Technology Laboratory (NETL, USA), respectively. The proposed modeling approach is based on an experience-based simplification of the physical problem to reduce model computational efforts with minimal expense of accuracy. Traditional high fidelity dynamic modelling requires specialized skills and significant computational resources. This innovative approach, on the other hand, can be easily adapted to different plant configurations, predicting the most relevant dynamic phenomena with a reduced number of states: such a feature will allow, in the near future, the model deployment for monitoring purposes or advanced control scheme applications (e.g. model predictive control). The three target systems are briefly introduced and dynamic situations analyzed for model tuning, first, and validation, then. Relevance is given to peculiar transients where the model shows its reliability and its weakness. Assumptions introduced during model definition for the three different test-rigs are discussed and compared. The model captured significant dynamic behavior in all analyzed systems (in particular those regarding the GT) and showed influence of signal noise on some of the SOFC computed outputs.

This paper presents the development, implementation and validation of a simplified dynamic modeling approach to describe SOFC/GT hybrid systems in three real emulator test-rigs installed at University of Genoa (UNIGE, Italy), German Aerospace Center (DLR, Germany) and National Energy Technology Laboratory (NETL, USA), respectively. The proposed modeling approach is based on an experience-based simplification of the physical problem to reduce model computational efforts with minimal expense of accuracy. Traditional high fidelity dynamic modelling requires specialized skills and significant computational resources. This innovative approach, on the other hand, can be easily adapted to different plant configurations, predicting the most relevant dynamic phenomena with a reduced number of states: such a feature will allow, in the near future, the model deployment for monitoring purposes or advanced control scheme applications (e.g. model predictive control). The three target systems are briefly introduced and dynamic situations analyzed for model tuning, first, and validation, then. Relevance is given to peculiar transients where the model shows its reliability and its weakness. Assumptions introduced during model definition for the three different test-rigs are discussed and compared. The model captured significant dynamic behavior in all analyzed systems (in particular those regarding the GT) and showed influence of signal noise on some of the SOFC computed outputs.