LAUSR

Abstract The experimental implementation of real-time optimization (RTO) to a commercial solid-oxide fuel-cell (SOFC) system is reported in this paper. The goal of RTO is to maximize the system efficiency at steady state subject to several operating constraints. The proposed RTO strategy is a constraint-adaptation approach, which consists in adding bias correction terms to the constraints in the optimization problem. These bias terms are estimated during operation using transient measurements in combination with a dynamic model. The scheme enforces plant optimality by continuously detecting and tracking the set of active constraints. This approach drives the fuel-cell system quickly to the desired power demand, while maximizing the efficiency and paying attention to constraint satisfaction. As such, this RTO scheme has the ability to both control and optimize fuel-cell systems. This experimental system reached about 65% efficiency. In addition, it was possible to deal with slow drifts such as degradation without compromising on optimality.