Enforcing optimal operation in solid-oxide fuel-cell systems. (15th August 2019)

• Record Type: Journal Article
• Authors: de Avila Ferreira, Tafarel; Wuillemin, Zacharie; Faulwasser, Timm; Salzmann, Christophe; Van herle, Jan; Bonvin, Dominique
• Is Part Of: Energy. Volume 181(2019)
• Page Start: 281
• Page End: 293
• Publication Date: 2019-08-15
• Subjects: SOFC system -- Optimal efficiency -- Real-time optimization -- Constraint adaptation -- Plant-model mismatch -- Time-scale separation; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.04.188
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper describes an optimization strategy for operating solid-oxide fuel-cell systems at optimal efficiency. Specifically, we present the experimental validation of a real-time optimization (RTO) strategy applied to a commercial solid-oxide fuel-cell system. The proposed RTO scheme effectively pushes the system to higher levels of efficiency and maintains the system there despite perturbations by tracking active constraints. The optimization approach uses either steady-state measurements, or transient measurements in combination with a dynamic model, and can deal effectively with plant-model mismatch. In the reported experiments, the approach drives the system to the desired power demand at optimal efficiency. The experimental fuel-cell system reached 65% DC electrical efficiency. As such, the proposed RTO scheme is a promising candidate for enforcing optimal micro-CHP operation. In addition, the approach can deal with slow drifts such as degradation without compromising on efficiency. Finally, and important from a practical point of view, we suggest guidelines for safe and optimal operation. Highlights: Real-time optimization is applied to a complete commercial experimental SOFC system. An alternative RTO scheme, fast constraint adaptation, is proposed. Use of transient measurements to increase the convergence speed of the process. Fast constraint adaptation deals with slow drifts without compromising on efficiency. The proposed approach pushes the operational efficiency to its maximum.