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Optimal off‐line trajectory planning of hybrid fuel cell/gas turbine power plants
Author(s) -
Kameswaran Shivakumar,
Biegler L. T.,
Junker S. Tobias,
GhezelAyagh Hossein
Publication year - 2007
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.11089
Subject(s) - solver , collocation (remote sensing) , matlab , mathematical optimization , computer science , optimal control , discretization , trajectory , dynamic programming , optimization problem , trajectory optimization , control theory (sociology) , control engineering , engineering , mathematics , control (management) , mathematical analysis , physics , astronomy , machine learning , artificial intelligence , operating system
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. © 2007 American Institute of Chemical Engineers AIChE J, 2007