Abstract
Pressurized solid oxide fuel cell (SOFC) systems are a particularly attractive conversion technology for their high electric efficiency, potential for cogeneration applications, low carbon emissions, and high performance at part-load. In this work, an innovative biofueled hybrid system is considered, where the fuel cell stack is pressurized with a turbocharger, resulting in a system with improved cost effectiveness than a microturbine-based one at small scales. In a previous work, a detailed steady-state model of the system, featuring components validated with industrial data, was developed to simulate the system and analyze its behavior in different conditions. The results obtained from this model were used to create response surfaces capable of evaluating the impact of the main operating parameters (fuel cell area, stack current density, and recuperator (REC) surface) on the performance and the profitability of the plant considering system uncertainties. In this paper, similar but extended response surfaces will be used to perform a multi-objective optimization of the system considering the capital costs of the plant and the net power produced as objectives (turbocharger is fixed in geometry). The impact of the energy market scenario on the optimal design of such a system will be investigated considering its installation in three different countries. Finally, the Pareto front produced by optimization will be used to evaluate the robustness of the top performance solutions.
Issue Section:
Research Papers
References
1.
Cuneo
,
A.
,
Traverso
,
A.
, and
Shahpar
,
S.
, 2017
, “
Comparative Analysis of Methodologies for Uncertainty Propagation and Quantification
,” ASME
Paper No. GT2017-63238.10.1115/GT2017-632382.
Ghanem
,
R.
,
Owhadi
,
H.
, and
Higdon
,
D.
, 2017
, Handbook of Uncertainty Quantification
, Springer International Publishing, Switzerland.3.
Cuneo
,
A.
,
Mantelli
,
L.
,
Giugno
,
A.
, and
Traverso
,
A.
, 2019
, “
Uncertainty Quantification Analysis of a Pressurised Fuel Cell Hybrid System
,” ASME
Paper No. GTP-19-1597.10.1115/GTP-19-15974.
Zhang
,
X.
,
Chan
,
S. H.
,
Li
,
G.
,
Ho
,
H. K.
,
Li
,
J.
, and
Feng
,
Z.
, 2010
, “
A Review of Integration Strategies for Solid Oxide Fuel Cells
,” J. Power Sources
,
195
(3
), pp. 685
–702
.10.1016/j.jpowsour.2009.07.0455.
Mehta
,
V.
, and
Cooper
,
J. S.
, 2003
, “
Review and Analysis of PEM Fuel Cell Design and Manufacturing
,” J. Power Sources
,
114
(1
), pp. 32
–53
.10.1016/S0378-7753(02)00542-66.
Stambouli
,
A. B.
, and
Traversa
,
E.
, 2002
, “
Solid Oxide Fuel Cells (SOFCs): A Review of an Environmentally Clean and Efficient Source of Energy
,” Renewable Sustainable Energy Rev.
,
6
(5
), pp. 433
–455
.10.1016/S1364-0321(02)00014-X7.
Cuneo
,
A.
,
Zaccaria
,
V.
,
Tucker
,
D.
, and
Sorce
,
A.
, 2018
, “
Gas Turbine Size Optimization in a Hybrid System Considering SOFC Degradation
,” Appl. Energy
,
230
, pp. 855
–864
.10.1016/j.apenergy.2018.09.0278.
Damo
,
U. M.
,
Ferrari
,
M. L.
,
Turan
,
A.
, and
Massardo
,
A. F.
, 2019
, “
Solid Oxide Fuel Cell Hybrid System: A Detailed Review of an Environmentally Clean and Efficient Source of Energy
,” Energy
,
168
, pp. 235
–246
.10.1016/j.energy.2018.11.0919.
Minh
,
N. Q.
, 2004
, “
Solid Oxide Fuel Cell Technology—Features and Applications
,” Solid State Ionics
,
174
(1–4
), pp. 271
–277
.10.1016/j.ssi.2004.07.04210.
Faes
,
A.
,
Fuerbringer
,
J. M.
,
Mohamedi
,
D.
,
Hessler-Wyser
,
A.
,
Caboche
,
G.
, and
Van Herle
,
J.
, 2011
, “
Design of Experiment Approach Applied to Reducing and Oxidizing Tolerance of Anode Supported Solid Oxide Fuel Cell. Part I: Microstructure Optimization
,” J. Power Sources
,
196
(17
), pp. 7058
–7069
.10.1016/j.jpowsour.2010.07.09211.
Charoen
,
K.
,
Prapainainar
,
C.
,
Sureeyatanapas
,
P.
,
Suwannaphisit
,
T.
,
Wongamornpitak
,
K.
,
Kongkachuichay
,
P.
,
Holmes
,
S. M.
, and
Prapainainar
,
P.
, 2017
, “
Application of Response Surface Methodology to Optimize Direct Alcohol Fuel Cell Power Density for Greener Energy Production
,” J. Cleaner Prod.
,
142
(Part 3
), pp. 1309
–1320
.10.1016/j.jclepro.2016.09.05912.
Xuan
,
D.
,
Li
,
Z.
,
Kim
,
J.
, and
Kim
,
Y.
, 2009
, “
Optimal Operating Points of PEM Fuel Cell Model With RSM
,” J. Mech. Sci. Technol.
,
23
(3
), pp. 717
–728
.10.1007/s12206-009-0205-y13.
Fadaee
,
M.
, and
Radzi
,
M. A. M.
, 2012
, “
Multi-Objective Optimization of a Stand-Alone Hybrid Renewable Energy System by Using Evolutionary Algorithms: A Review
,” Renewable Sustainable Energy Rev.
,
16
(5
), pp. 3364
–3369
.10.1016/j.rser.2012.02.07114.
Shirazi
,
A.
,
Aminyavari
,
M.
,
Najafi
,
B.
,
Rinaldi
,
F.
, and
Razaghi
,
M.
, 2012
, “
Thermal-Economic-Environmental Analysis and Multi-Objective Optimization of an Internal-Reforming Solid Oxide Fuel Cell-Gas Turbine Hybrid System
,” Int. J. Hydrogen Energy
,
37
(24
), pp. 19111
–19124
.10.1016/j.ijhydene.2012.09.14315.
Sharafi
,
M.
, and
ELMekkawy
,
T. Y.
, 2014
, “
Multi-Objective Optimal Design of Hybrid Renewable Energy Systems Using PSO-Simulation Based Approach
,” Renewable Energy
,
68
, pp. 67
–79
.10.1016/j.renene.2014.01.01116.
Oh
,
S. R.
,
Sun
,
J.
,
Dobbs
,
H.
, and
King
,
J.
, 2011
, “
Performance Evaluation of Solid Oxide Fuel Cell Engines Integrated With Single/Dual-Spool Turbochargers
,” ASME J. Fuel Cell Sci. Technol.
,
8
(6
), p. 061020
.10.1115/1.400447117.
Gandiglio
,
M.
,
Lanzini
,
A.
,
Leone
,
P.
,
Santarelli
,
M.
, and
Borchiellini
,
R.
, 2013
, “
Thermoeconomic Analysis of Large Solid Oxide Fuel Cell Plants: Atmospheric vs. Pressurized Performance
,” Energy
,
55
, pp. 142
–155
.10.1016/j.energy.2013.03.05918.
Henke
,
M.
,
Kallo
,
J.
,
Friedrich
,
K. A.
, and
Bessler
,
W. G.
, 2011
, “
Influence of Pressurisation on SOFC Performance and Durability: A Theoretical Study
,” Fuel Cells
,
11
(4
), pp. 581
–591
.10.1002/fuce.20100009819.
Venkataraman
,
K.
,
Wanat
,
E. C.
, and
Schmidt
,
L. D.
, 2003
, “
Steam Reforming of Methane and Water-Gas Shift in Catalytic Wall Reactors
,” AIChE J.
,
49
(5
), pp. 1277
–1284
.10.1002/aic.69049051820.
Staxera
, “SOFC Stack—Mk200, Product Data Sheet, Article 186,” Staxera, Dresden, Germany.21.
Sorce
,
A.
,
Greco
,
A.
,
Magistri
,
L.
, and
Costamagna
,
P.
, 2014
, “
FDI Oriented Modeling of an Experimental SOFC System, Model Validation and Simulation of Faulty States
,” Appl. Energy
,
136
, pp. 894
–908
.10.1016/j.apenergy.2014.03.07422.
Greco
,
A.
,
Sorce
,
A.
,
Littwin
,
R.
,
Costamagna
,
P.
, and
Magistri
,
L.
, 2014
, “
Reformer Faults in SOFC Systems: Experimental and Modeling Analysis, and Simulated Fault Maps
,” Int. J. Hydrogen Energy
,
39
(36
), pp. 21700
–21713
.10.1016/j.ijhydene.2014.09.06323.
Ferrari
,
M. L.
,
Pascenti
,
M.
, and
Massardo
,
A. F.
, 2008
, “
Ejector Model for High Temperature Fuel Cell Hybrid Systems: Experimental Validation at Steady-State and Dynamic Conditions
,” ASME J. Fuel Cell Sci. Technol.
,
5
(4
), p. 041005
.10.1115/1.289010224.
Traverso
,
A.
,
Massardo
,
A. F.
, and
Scarpellini
,
R.
, 2006
, “
Externally Fired Micro-Gas Turbine: Modelling and Experimental Performance
,” Appl. Therm. Eng.
,
26
(16
), pp. 1935
–1941
.10.1016/j.applthermaleng.2006.01.01325.
Ferrari
,
M. L.
,
De Campo
,
M.
, and
Magistri
,
L.
, 2018
, “
Design and Emulation of a Turbocharged Bio-Fuelled SOFC Plant
,” ASME
Paper No. GT2018-75026.10.1115/GT2018-7502626.
Khuri
,
A. I.
, and
Mukhopadhyay
,
S.
, 2010
, “
Response Surface Methodology
,” Wiley Interdiscip. Rev. Comput. Stat.
,
2
(2
), pp. 128
–149
.10.1002/wics.7327.
Gunst
,
R. F.
,
Myers
,
R. H.
, and
Montgomery
,
D. C.
, 2006
, “
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
,” Technometrics
,
38
(3
), pp. 284
–286
.10.1080/00401706.1996.1048450928.
Giugno
,
A.
,
Cuneo
,
A.
, and
Traverso
,
A.
, 2019
, “
Analysis of Uncertainties in Compact Plate-Fin Recuperators for Microturbines
,” Appl. Therm. Eng.
,
150
, pp. 1243
–1251
.10.1016/j.applthermaleng.2019.01.09329.
Cuneo
,
A.
,
Zaccaria
,
V.
,
Tucker
,
D.
, and
Traverso
,
A.
, 2017
, “
Probabilistic Analysis of a Fuel Cell Degradation Model for Solid Oxide Fuel Cell and Gas Turbine Hybrid Systems
,” Energy
,
141
, pp. 2277
–2287
.10.1016/j.energy.2017.12.00230.
Arsalis
,
A.
, 2008
, “
Thermoeconomic Modeling and Parametric Study of Hybrid SOFC-Gas Turbine-Steam Turbine Power Plants Ranging From 1.5 to 10 MWe
,” J. Power Sources
,
181
(2
), pp. 313
–326
.10.1016/j.jpowsour.2007.11.10431.
Galanti
,
L.
, and
Massardo
,
A. F.
, 2011
, “
Micro Gas Turbine Thermodynamic and Economic Analysis Up to 500 KWe Size
,” Appl. Energy
,
88
(12
), pp. 4795
–4802
.10.1016/j.apenergy.2011.06.02232.
Giugno
,
A.
,
Mantelli
,
L.
,
Cuneo
,
A.
, and
Traverso
,
A.
, 2019
, “
Robust Design of a Hybrid Energy System
,” E3S Web Conf.
,
113
, p. 02008
.10.1051/e3sconf/20191130200833.
Chemical Engineering, 2020, “CEPCI Archives—Chemical Engineering,” Chemical Engineering, New York,
accessed Jan. 27, 2020, https://www.chemengonline.com/tag/cepci/34.
Eurostat—Statistical Office of the European Communities
, 2019
, “Electricity Prices Components for Non-Household Consumers—Annual Data,” Eurostat—Statistical Office of the European Communities, Luxembourg.35.
EIA—U.S. Energy Information Administration
, 2019
, “Average Price of Electricity to Ultimate Customers by End-Use Sector,” EIA—U.S. Energy Information Administration, Washington, DC.36.
Eurostat—Statistical Office of the European Communities
, 2019
, “Gas Prices Components for Non-Household Consumers—Annual Data,” Eurostat—Statistical Office of the European Communities, Luxembourg.37.
EIA—U.S. Energy Information Administration
, 2019
, “Natural Gas Prices,” EIA—U.S. Energy Information Administration, Washington, DC.38.
StatEase, 2020, “Interpretation of R-Squared,” StatEase, Minneapolis, MN,
accessed Jan. 14, 2020, https://www.statease.com/docs/v11/contents/analysis/interpretation-of-r-squared/39.
Murata
,
T.
, and
Ishibuchi
,
H.
, 1995
, “
MOGA: Multi-Objective Genetic Algorithms
,” Proceedings of the IEEE Conference on Evolutionary Computation
, Perth, WA, Nov. 29–Dec. 1, pp. 289
–294
.10.1109/ICEC.1995.48916140.
Fonseca
,
C. M.
, and
Fleming
,
P. J.
, 1993
, “
Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
,” Proceedings of the ICGA-93: Fifth International Conference on Genetic Algorithms
, San Mateo, CA, July 17–22, pp. 416
–423
.10.5555/645513.65775741.
Cassettari
,
L.
,
Mosca
,
R.
, and
Revetria
,
R.
, 2012
, “
Monte Carlo Simulation Models Evolving in Replicated Runs: A Methodology to Choose the Optimal Experimental Sample Size
,” Math. Probl. Eng.
,
2012
, pp. 1
–17
.10.1155/2012/463873Copyright © 2021 by ASME
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