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Advanced Control for Clusters of SOFC/Gas Turbine Hybrid Systems
University of Genoa, Genova, Italy.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-6101-2863
University of Genoa, Genova, Italy.
2018 (English)In: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 140, no 5, article id 051703Article in journal (Refereed) Published
Abstract [en]

The use of model predictive control (MPC) in advanced power systems can be advantageous in controlling highly coupled variables and optimizing system operations. Solid oxide fuel cell/gas turbine (SOFC/GT) hybrids are an example where advanced control techniques can be effectively applied. For example, to manage load distribution among several identical generation units characterized by different temperature distributions due to different degradation paths of the fuel cell stacks. When implementing an MPC, a critical aspect is the trade-off between model accuracy and simplicity, the latter related to a fast computational time. In this work, a hybrid physical and numerical approach was used to reduce the number of states necessary to describe such complex target system. The reduced number of states in the model and the simple framework allow real-time performance and potential extension to a wide range of power plants for industrial application, at the expense of accuracy losses, discussed in the paper. 

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME) , 2018. Vol. 140, no 5, article id 051703
Keywords [en]
Economic and social effects; Fuel cells; Hybrid systems; Industrial plants; Model predictive control; Predictive control systems; Turbines, Advanced control; Computational time; Generation units; Load distributions; Number of state; Numerical approaches; Real time performance; System operation, Solid oxide fuel cells (SOFC)
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-38576DOI: 10.1115/1.4038321ISI: 000428871900012Scopus ID: 2-s2.0-85040680634OAI: oai:DiVA.org:mdh-38576DiVA, id: diva2:1181356
Available from: 2018-02-08 Created: 2018-02-08 Last updated: 2018-04-26Bibliographically approved

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Zaccaria, Valentina

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