A nonlinear programming (NLP) framework is developed to determine optimal operating policies for hybrid fuel cell/gas turbine power systems. The approach consists of a dynamic model of the power plant, reformulated as an index one differential algebraic equation (DAE) system. A dynamic optimization framework is developed where the constraints include the dynamic model of the plant. The system model is then discretized using Radau collocation on finite elements and formulated in the AMPL modeling environment. This allows for the straightforward solution of dynamic optimization problems using large-scale NLP solvers. IPOPT is the NLP solver used in this study. Program links were provided to Matlab/Simulink to visualize and interpret the results. The formulation of a dynamic optimization problem was focused on determination of optimal operating trajectories for tracking power plant load variations. Efficiency measures were also included as a part of the dynamic optimization problem to maximize efficiency while tracking the desired load profile. Results from 18 case studies show that the dynamic optimization can be performed quickly with excellent results. The applicability of the dynamic optimization framework for the estimation of feed fuel concentrations is also demonstrated. (c) 2007 American Institute of Chemical Engineers.