scholarly journals Technical Development Issues and Dynamic Modeling of Gas Turbine and Fuel Cell Hybrid Systems

1999 ◽  
Author(s):  
Eric A. Liese ◽  
Randall S. Gemmen ◽  
Faryar Jabbari ◽  
Jacob Brouwer
Keyword(s):  
Fuel Cell ◽  
Gas Turbine ◽  
Hybrid Systems ◽  
Dynamic Modeling ◽  
Hybrid System ◽  
Dynamic Models ◽  
Cell Model ◽  
Dimensional System ◽  
Modeling Tools ◽  
Steam Reformer

This paper describes safety issues important to the operation of combined fuel cell and gas turbine (hybrid) systems, and provides motivation for building dynamic modeling tools to support their development. It also describes two models — a steam reformer and a fuel cell — that will be used to investigate the dynamic performance of a hybrid system. The present goals are to develop dynamic models for these two components, ensure their reliability, and obtain a basic understanding of their performance prior to integration into a complete hybrid system model. Because of the large physical domain to be analyzed in the integrated hybrid system, both reformer and fuel cell models are simplified to a one-dimensional system of equations. Model results are presented for a tubular, counterflow steam reformer showing methane conversion and temperature behavior during initial startup, and following several step change perturbations. For the fuel cell model, a generic planar type is analyzed showing voltage and current behavior following step changes in load resistance and fuel input. The results provide confidence in each model’s reliability, enabling them to be integrated for hybrid system simulation. Results from the integrated simulations will provide guidance on future hybrid technology development needs.

Development of Dynamic Modeling Tools for Solid Oxide and Molten Carbonate Hybrid Fuel Cell Gas Turbine Systems

2000 ◽  
Author(s):  
Randall S. Gemmen ◽  
Eric Liese ◽  
Jose G. Rivera ◽  
Faryar Jabbari ◽  
Jacob Brouwer
Keyword(s):  
Fuel Cell ◽  
Gas Turbine ◽  
Dynamic Modeling ◽  
Hybrid System ◽  
Dynamic Models ◽  
Load Resistance ◽  
Solid Oxide ◽  
Molten Carbonate Fuel Cell ◽  
Molten Carbonate ◽  
Modeling Tools

This paper describes some generic solid oxide and molten carbonate hybrid fuel cell gas turbine systems and dynamic modeling tools that are being developed to simulate the performance of these and other hybrid fuel cell systems. The generic hybrid systems are presented to introduce issues and technical development challenges that hybrid fuel cell gas turbine systems must address and to provide a platform for the development of the dynamic modeling tools. The present goals are to develop dynamic models for the basic components of solid oxide and molten carbonate fuel cell gas turbine hybrids, ensure their reliability, and obtain a basic understanding of their performance prior to integration into a complete hybrid system model. Preliminary results for molten carbonate and solid oxide fuel cell types are presented. These results provide understanding of some of the operational characteristics of fuel cells, and indicate the complexity of the dynamic response of fuel cell hybrid components. For the fuel cell models, generic planar designs are analyzed showing voltage and current behavior following step changes in load resistance and steady state performance curves. The results provide confidence in each of the model’s reliability, enabling them to be integrated for hybrid system simulation. Results from the integrated simulations will provide guidance on future hybrid technology development needs.

Performance Study of Hybrid Solid Oxide Fuel Cell–Gas Turbine Power System

2010 ◽  
Vol 171-172 ◽  
pp. 319-322
Author(s):  
Hong Bin Zhao ◽  
Xu Liu
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Hybrid System ◽  
Cell Model ◽  
Research Work ◽  
Atmospheric Condition ◽  
Operating Conditions ◽  
Solid Oxide ◽  
Oxide Fuel

The simulation and analyses of a “bottoming cycle” solid oxide fuel cell–gas turbine (SOFC–GT) hybrid system at the standard atmospheric condition is presented in this paper. The fuel cell model used in this research work is based on a tubular Siemens–Westinghouse–type SOFC with 1.8MW capacity. Energy and exergy analyses of the whole system at fixed conditions are carried out. Then, comparisons of the exergy destruction and exergy efficiency of each component are also conducted to determine the potential capability of the hybrid system to generate power. Moreover, the effects of operating conditions including fuel flow rate and SOFC operating temperature on performances of the hybrid system are analyzed.

Cycle analysis of solid oxide fuel cell-gas turbine hybrid systems integrated ethanol steam reformer: Energy management

Energy ◽  
2017 ◽  
Vol 127 ◽  
pp. 743-755 ◽  
Author(s):  
Dang Saebea ◽  
Loredana Magistri ◽  
Aristide Massardo ◽  
Amornchai Arpornwichanop
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Hybrid Systems ◽  
Energy Management ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Steam Reformer ◽  
Ethanol Steam

Simulation of Distributive Generation Using Fuel Cell Hybrid Systems

2006 ◽  
Author(s):  
Brian Wolf ◽  
Shripad Revankar
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Cell Hybrid ◽  
Cell Model ◽  
Initial Step ◽  
Parametric Studies ◽  
Key Issues ◽  
Fuel Cell Hybrid

Fuel cell hybrid technology has the potential to significantly change our current energy infrastructure. Past studies have shown that the combination of fuel cells and turbines can produce power at remarkably high efficiencies with low levels of pollution. The work presented in this paper is an initial step to further development of a hybrid system model. The fuel cell model discussed is used to perform parametric studies to aid in the optimization of a hybrid system. This paper provides an overview of fuel cell hybrid systems and distributive generation. A fuel cell model is implemented in SIMULINK using basic balance equations. Key issues of modeling specifically high temperature fuel cells are discussed along with their transient response and how it may affect the performance of a distributive generation system.

Dynamic modeling of a hybrid system of the solid oxide fuel cell and recuperative gas turbine

2006 ◽  
Vol 163 (1) ◽  
pp. 523-531 ◽  
Author(s):  
Xiongwen Zhang ◽  
Jun Li ◽  
Guojun Li ◽  
Zhenping Feng
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Dynamic Modeling ◽  
Hybrid System ◽  
Solid Oxide ◽  
Oxide Fuel

Inter-Laboratory Dynamic Modeling of a Carbonate Fuel Cell for Hybrid Application

2003 ◽  
Author(s):  
Rory A. Roberts ◽  
Faryar Jabbari ◽  
Jacob Brouwer ◽  
Randall S. Gemmen ◽  
Eric A. Liese
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Dynamic Response ◽  
Gas Turbine ◽  
Hybrid System ◽  
Dynamic Models ◽  
Detailed Comparison ◽  
Simulation Software ◽  
Molten Carbonate ◽  
Carbonate Fuel Cell

A detailed comparison of dynamic models developed for carbonate fuel cells used in hybrid fuel cell gas turbine systems is presented. The two models are nearly similar in that both treat the bulk behavior of the system (e.g., through lumped or one-dimensional solutions of the fundamental equations. However, both models are implemented independently by different research groups using disparate simulation software programs. As a test case for the comparison, a generic molten carbonate hybrid fuel cell gas turbine system is identified. Such comparison-work benefits all parties by ensuring sub-model reliability prior to integration into a complete hybrid system model. Detailed results for the carbonate fuel cell models are presented. For a generic planar design, voltage and current behavior are shown following step changes in load resistance and fuel flow. The time scales for thermal dynamic response are much greater than those required for the initial electrochemical dynamic response as is expected. These results provide understanding of some of the operational characteristics of fuel cells and indicate the complexity of the dynamic response of fuel cell hybrid components. The results from the two models are not identical, but compare sufficiently well to provide confidence in each of the model’s reliability, enabling them to be integrated for hybrid system simulation. Results from the integrated simulations will provide guidance on future hybrid technology development needs.

A Degradation Diagnosis Method for Gas Turbine - Fuel Cell Hybrid Systems Using Bayesian Networks

2021 ◽  
Author(s):  
Luca Mantelli ◽  
Valentina Zaccaria ◽  
Mario L. Ferrari ◽  
Konstantinos G. Kyprianidis
Keyword(s):  
Fuel Cell ◽  
Gas Turbine ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Essential Information ◽  
Belief Network ◽  
Starting Point

Abstract This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell - gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.

A Degradation Diagnosis Method for Gas Turbine – Fuel Cell Hybrid Systems Using Bayesian Networks

2020 ◽  
Author(s):  
L. Mantelli ◽  
V. Zaccaria ◽  
K. Kyprianidis ◽  
M. L. Ferrari
Keyword(s):  
Fuel Cell ◽  
Gas Turbine ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Cell Hybrid ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Sensor Fault ◽  
Belief Network

Abstract During the last decades there has been a rise of awareness regarding the necessity to increase energy systems efficiency and reduce carbon emissions. These goals could be partially achieved through a greater use of gas turbine - solid oxide fuel cell hybrid systems to generate both electric power and heat. However, this kind of systems are known to be delicate, especially due to the fragility of the cell, which could be permanently damaged if its temperature and pressure levels exceed their operative limits. This could be caused by degradation of a component in the system (e.g. the turbomachinery), but also by some sensor fault which leads to a wrong control action. To be considered commercially competitive, these systems must guarantee high reliability and their maintenance costs must be minimized. Thus, it is necessary to integrate these plants with an automated diagnosis system capable to detect degradation levels of the many components (e.g. turbomachinery and fuel cell stack) in order to plan properly the maintenance operations, and also to recognize a sensor fault. This task can be very challenging due to the high complexity of the system and the interactions between its components. Another difficulty is related to the lack of sensors, which is common on commercial power plants, and makes harder the identification of faults in the system. This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell – gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.

Fuel-Cell Hybrid Systems to Generate Shipboard Electrical Power

2009 ◽  
Author(s):  
W. J. Sembler ◽  
S. Kumar
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Air Quality ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Cell Hybrid ◽  
Electrical Power ◽  
History Of ◽  
Marine Applications ◽  
Fuel Cell Hybrid

The reduction of shipboard airborne emissions has been receiving increased attention due to the desire to improve air quality and reduce the generation of greenhouse gases. The use of a fuel cell could represent an environmentally friendly way for a ship to generate in-port electrical power that would eliminate the need to operate diesel-driven generators or use shore power. This paper includes a brief description of the various types of fuel cells in use today, together with a review of the history of fuel cells in marine applications. In addition, the results of a feasibility study conducted to evaluate the use of a fuel-cell hybrid system to produce shipboard electrical power are presented.

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