Document Type : Original Article
Authors
1 College of Mechanical and Energy Engineering, Shaoyang University, Shaoyang 422000, Hunan, China
2 Key Laboratory of Hunan Province for Efficient Power System and Intelligent Manufacturing, Shaoyang 422000, Hunan, China
10.52293/SE.1.1.1529
Abstract
This paper presents an analysis of improving the efficiency of a hybrid solid oxide fuel cell (SOFC) and a micro gas turbine (mGT) system. The main reason for using SOFC technology is the generation of its less harmful products with higher performance compared to the traditional power generation systems. In addition, the combination of the gas turbine can improve the SOFC system’s reliability. Due to the importance of SOFC systems degradation in the industry, using the optimized hybrid system to reduce SOFC degradation is a proper process. This study presents a new developed bio-inspired optimization technique based on the rhino herd algorithm. After validation of the method with some different bio-inspired methods, it is employed to optimal size selection of the gas turbine for the fuel cell system reliability. Simulation results show that using a larger size of the turbine gives a higher level of power to the SOFC. It also decreases the efficiency of the initial turbine and increases the initial capital investment.
Keywords
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[25] A. Nouri, H. Khodaei, A. Darvishan, S. Sharifian, and N. Ghadimi, “Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: an epsilon constraint method and fuzzy satisfying approach,” Energy, vol. 159, pp. 121-133, 2018.
[26] N. Ghadimi, “Genetically tuning of lead-lag controller in order to control of fuel cell voltage,” Scientific Research and Essays, vol. 7, pp. 3695-3701, 2012.
[27] O.-J. Kwon, H.-S. Shin, S.-H. Cheon, and B. S. Oh, “A study of numerical analysis for PEMFC using a multiphysics program and statistical method,” International Journal of Hydrogen Energy, vol. 40, pp. 11577-11586, 2015.
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[30] M. A. Abreu-Sepulveda, N. F. Harun, G. Hackett, A. Hagen, and D. Tucker, “Accelerated Degradation for Hardware in the Loop Simulation of Fuel Cell-Gas Turbine Hybrid System,” Journal of Fuel Cell Science and Technology, vol. 12, p. 021001, 2015.
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[35] X. Fei, R. Xuejun, and N. Razmjooy, “Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1-21, 2019.
[36] A. Namadchian, M. Ramezani, and N. Razmjooy, “A New Meta-Heuristic Algorithm for Optimization Based on Variance Reduction of Guassian Distribution,” Majlesi Journal of Electrical Engineering, vol. 10, p. 49, 2016.
[37] A. Ahadi, N. Ghadimi, and D. Mirabbasi, “An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability,” Complexity, vol. 21, pp. 99-113, 2015.
[38] A. Ahadi, N. Ghadimi, and D. Mirabbasi, “Reliability assessment for components of large scale photovoltaic systems,” Journal of Power Sources, vol. 264, pp. 211-219, 2014.
[39] I. Ahmadian, O. Abedinia, and N. Ghadimi, “Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization,” Frontiers in Energy, vol. 8, pp. 412-425, 2014.
[40] M.-W. Tian, S.-R. Yan, S.-Z. Han, S. Nojavan, K. Jermsittiparsert, and N. Razmjooy, “New Optimal Design for a Hybrid Solar Chimney, Solid Oxide Electrolysis and Fuel Cell based on Improved Deer hunting optimization algorithm,” Journal of Cleaner Production, p. 119414, 2019.
[41] D. Yu, Y. Wang, H. Liu, K. Jermsittiparsert, and N. Razmjooy, “System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm,” Energy Reports, vol. 5, pp. 1365-1374, 2019.
[42] M. Dideban, N. Ghadimi, M. B. Ahmadi, and M. Karimi, “Optimal location and sizing of shunt capacitors in distribution systems by considering different load scenarios,” Journal of Electrical Engineering and Technology, vol. 8, pp. 1012-1020, 2013.
[43] M. Eskandari Nasab, I. Maleksaeedi, M. Mohammadi, and N. Ghadimi, “A new multiobjective allocator of capacitor banks and distributed generations using a new investigated differential evolution,” Complexity, vol. 19, pp. 40-54, 2014.
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[45] N. Razmjooy, M. Ramezani, and N. Ghadimi, “Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal,” International Journal of Fuzzy Systems, vol. 19, pp. 1144-1156, 2017.
[46] N. Razmjooy, F. R. Sheykhahmad, and N. Ghadimi, “A hybrid neural network–world cup optimization algorithm for melanoma detection,” Open Medicine, vol. 13, pp. 9-16, 2018.
[47] N. Razmjooy, M. Khalilpour, and M. Ramezani, “A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System,” Journal of Control, Automation and Electrical Systems, vol. 27, pp. 419-440, 2016.
[48] M. A. El Aziz, A. A. Ewees, and A. E. Hassanien, “Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation,” Expert Systems with Applications, vol. 83, pp. 242-256, 2017.
[49] G. Dhiman and V. Kumar, “Emperor penguin optimizer: A bio-inspired algorithm for engineering problems,” Knowledge-Based Systems, vol. 159, pp. 20-50, 2018.
[50] G.-G. Wang, X.-Z. Gao, K. Zenger, and L. d. S. Coelho, “A novel metaheuristic algorithm inspired by rhino herd behavior,” in Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016, 2018, pp. 1026-1033.
[51] J. S. Horne, E. O. Garton, and J. L. Rachlow, “A synoptic model of animal space use: simultaneous estimation of home range, habitat selection, and inter/intra-specific relationships,” Ecological Modelling, vol. 214, pp. 338-348, 2008.
[52] D. Yang, G. Li, and G. Cheng, “On the efficiency of chaos optimization algorithms for global optimization,” Chaos, Solitons & Fractals, vol. 34, pp. 1366-1375, 2007.
[53] C. Rim, S. Piao, G. Li, and U. Pak, “A niching chaos optimization algorithm for multimodal optimization,” Soft Computing, vol. 22, pp. 621-633, 2018.
[54] C. Choi and J.-J. Lee, “Chaotic local search algorithm,” Artificial Life and Robotics, vol. 2, pp. 41-47, 1998.
[55] X. Li, P. Niu, and J. Liu, “Combustion optimization of a boiler based on the chaos and Levy flight vortex search algorithm,” Applied Mathematical Modelling, vol. 58, pp. 3-18, 2018.
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[58] J. C. Bansal, “Particle Swarm Optimization,” in Evolutionary and Swarm Intelligence Algorithms, ed: Springer, 2019, pp. 11-23.
[2] C.-L. Lu, C.-P. Chang, Y.-H. Guo, T.-K. Yeh, Y.-C. Su, P.-C. Wang, et al., “High-performance and low-leakage phosphoric acid fuel cell with synergic composite membrane stacking of micro glass microfiber and nano PTFE,” Renewable energy, vol. 134, pp. 982-988, 2019.
[3] Y. Cao, Y. Li, G. Zhang, K. Jermsittiparsert, and N. Razmjooy, “Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm,” Energy Reports, vol. 5, pp. 1616-1625, 2019.
[4] Y. Cao, Y. Wu, L. Fu, K. Jermsittiparsert, and N. Razmjooy, “Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics,” Energy Reports, vol. 5, pp. 1551-1559, 2019.
[5] R. C. McDonald and M. Hamdan, “Compact Direct Methanol Fuel Cell: Design Approach Using Commercial Micropumps,” Journal of Electrochemical Energy Conversion and Storage, vol. 16, p. 011003, 2019.
[6] B. Ghorbani, M. Mehrpooya, and S. A. Mousavi, “Hybrid molten carbonate fuel cell power plant and multiple-effect desalination system,” Journal of Cleaner Production, vol. 220, pp. 1039-1051, 2019.
[7] A. Atkinson, S. Skinner, and J. Kilner, “Solid oxide fuel cells,” Fuel Cells and Hydrogen Production: A Volume in the Encyclopedia of Sustainability Science and Technology, Second Edition, pp. 569-589, 2019.
[8] D. Wang, B. Yu, W. Li, J. Shi, and J. Chen, “Heating performance evaluation of a CO2 heat pump system for an electrical vehicle at cold ambient temperatures,” Applied Thermal Engineering, vol. 142, pp. 656-664, 2018.
[9] Y. Ando, H. Oozawa, M. Mihara, H. Irie, Y. Urashita, and T. Ikegami, “Demonstration of SOFC-micro gas turbine (MGT) hybrid systems for commercialization,” Mitsubishi Heavy Industries Technical Review, vol. 52, pp. 47-52, 2015.
[10] D. Costa, R. Otto, A. Piardi, and R. Ramos, “A Survey of State-of-the-Art on Microgrids: Application in Real Time Simulation Environment,” in 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), 2019, pp. 1-6.
[11] L. Fiorini and M. Aiello, “Energy management for user’s thermal and power needs: A survey,” Energy Reports, vol. 5, pp. 1048-1076, 2019.
[12] J. Jia, L. Shu, G. Zang, L. Xu, A. Abudula, and K. Ge, “Energy analysis and techno-economic assessment of a co-gasification of woody biomass and animal manure, solid oxide fuel cells and micro gas turbine hybrid system,” Energy, vol. 149, pp. 750-761, 2018.
[13] R. Roberts and J. Brouwer, “Dynamic simulation of a pressurized 220kw solid oxide fuel-cell–gas-turbine hybrid system: modeled performance compared to measured results,” Journal of fuel cell science and technology, vol. 3, pp. 18-25, 2006.
[14] S. Harvey and H. Richter, “Gas turbine cycles with solid oxide fuel cells—part I: improved gas turbine power plant efficiency by use of recycled exhaust gases and fuel cell technology,” 1994.
[15] S. Harvey and H. Richter, “Gas turbine cycles with solid oxide fuel cells—part II: a detailed study of a gas turbine cycle with an integrated internal reforming solid oxide fuel cell,” 1994.
[16] N. Bessette and J. Pierre, “Status of siemens westinghouse tubular solid oxide fuel cell technology and development program,” in Proceedings of the 2000 Fuel Cell Seminar, Courtesy Associates, 2000.
[17] L. Barelli, G. Bidini, and A. Ottaviano, “Integration of SOFC/GT hybrid systems in Micro-Grids,” Energy, vol. 118, pp. 716-728, 2017.
[18] Z. Hajabdollahi and P.-F. Fu, “Multi-objective based configuration optimization of SOFC-GT cogeneration plant,” Applied Thermal Engineering, vol. 112, pp. 549-559, 2017.
[19] M. Hohloch, A. Huber, and M. Aigner, “Analysis of Operational Strategies of a SOFC/Micro Gas Turbine Hybrid Power Plant,” Journal of Engineering for Gas Turbines and Power, vol. 140, p. 081703, 2018.
[20] V. Zaccaria, D. Tucker, and A. Traverso, “Gas turbine advanced power systems to improve solid oxide fuel cell economic viability,” Journal of the Global Power and Propulsion Society, vol. 1, pp. 28-40, 2017.
[21] D. Tucker, M. Shelton, and A. Manivannan, “The role of solid oxide fuel cells in advanced hybrid power systems of the future,” The Electrochemical Society Interface, vol. 18, p. 45, 2009.
[22] M. Haghighi and F. Sharifhassan, “Exergy analysis and optimization of a high temperature proton exchange membrane fuel cell using genetic algorithm,” Case Studies in Thermal Engineering, vol. 8, pp. 207-217, 2016.
[23] J. M. Corrêa, F. A. Farret, L. N. Canha, and M. G. Simoes, “An electrochemical-based fuel-cell model suitable for electrical engineering automation approach,” IEEE Transactions on industrial electronics, vol. 51, pp. 1103-1112, 2004.
[24] F. Z. Aouali, M. Becherif, H. S. Ramadan, M. Emziane, A. Khellaf, and K. Mohammedi, “Analytical modelling and experimental validation of proton exchange membrane electrolyser for hydrogen production,” International Journal of Hydrogen Energy, vol. 42, pp. 1366-1374, 2017.
[25] A. Nouri, H. Khodaei, A. Darvishan, S. Sharifian, and N. Ghadimi, “Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: an epsilon constraint method and fuzzy satisfying approach,” Energy, vol. 159, pp. 121-133, 2018.
[26] N. Ghadimi, “Genetically tuning of lead-lag controller in order to control of fuel cell voltage,” Scientific Research and Essays, vol. 7, pp. 3695-3701, 2012.
[27] O.-J. Kwon, H.-S. Shin, S.-H. Cheon, and B. S. Oh, “A study of numerical analysis for PEMFC using a multiphysics program and statistical method,” International Journal of Hydrogen Energy, vol. 40, pp. 11577-11586, 2015.
[28] V. Zaccaria, D. Tucker, and A. Traverso, “A distributed real-time model of degradation in a solid oxide fuel cell, part I: Model characterization,” Journal of Power Sources, vol. 311, pp. 175-181, 2016.
[29] V. Zaccaria, A. Traverso, and D. Tucker, “A real-time degradation model for hardware in the loop simulation of fuel cell gas turbine hybrid systems,” in ASME Turbo Expo 2015: Turbine Technical Conference and Exposition, 2015.
[30] M. A. Abreu-Sepulveda, N. F. Harun, G. Hackett, A. Hagen, and D. Tucker, “Accelerated Degradation for Hardware in the Loop Simulation of Fuel Cell-Gas Turbine Hybrid System,” Journal of Fuel Cell Science and Technology, vol. 12, p. 021001, 2015.
[31] M. A. R. Nascimento, L. Rodrigues, E. Santos, E. E. B. Gomes, F. L. G. Dias, E. I. G. Velásques, et al., “Micro gas turbine engine: a review,” Progress in gas turbine performance, pp. 107-141, 2013.
[32] R. D, “Product specification models C600, C800, and C1000 capstone MicroTurbine,” Capstone, Ed., ed, 2009.
[33] V. Zaccaria, D. Tucker, and A. Traverso, “Operating strategies to minimize degradation in fuel cell gas turbine hybrids,” Applied energy, vol. 192, pp. 437-445, 2017.
[34] M. R. Weimar, L. A. Chick, D. W. Gotthold, and G. A. Whyatt, “Cost study for manufacturing of solid oxide fuel cell power systems,” Pacific Northwest National Lab.(PNNL), Richland, WA (United States)2013.
[35] X. Fei, R. Xuejun, and N. Razmjooy, “Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1-21, 2019.
[36] A. Namadchian, M. Ramezani, and N. Razmjooy, “A New Meta-Heuristic Algorithm for Optimization Based on Variance Reduction of Guassian Distribution,” Majlesi Journal of Electrical Engineering, vol. 10, p. 49, 2016.
[37] A. Ahadi, N. Ghadimi, and D. Mirabbasi, “An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability,” Complexity, vol. 21, pp. 99-113, 2015.
[38] A. Ahadi, N. Ghadimi, and D. Mirabbasi, “Reliability assessment for components of large scale photovoltaic systems,” Journal of Power Sources, vol. 264, pp. 211-219, 2014.
[39] I. Ahmadian, O. Abedinia, and N. Ghadimi, “Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization,” Frontiers in Energy, vol. 8, pp. 412-425, 2014.
[40] M.-W. Tian, S.-R. Yan, S.-Z. Han, S. Nojavan, K. Jermsittiparsert, and N. Razmjooy, “New Optimal Design for a Hybrid Solar Chimney, Solid Oxide Electrolysis and Fuel Cell based on Improved Deer hunting optimization algorithm,” Journal of Cleaner Production, p. 119414, 2019.
[41] D. Yu, Y. Wang, H. Liu, K. Jermsittiparsert, and N. Razmjooy, “System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm,” Energy Reports, vol. 5, pp. 1365-1374, 2019.
[42] M. Dideban, N. Ghadimi, M. B. Ahmadi, and M. Karimi, “Optimal location and sizing of shunt capacitors in distribution systems by considering different load scenarios,” Journal of Electrical Engineering and Technology, vol. 8, pp. 1012-1020, 2013.
[43] M. Eskandari Nasab, I. Maleksaeedi, M. Mohammadi, and N. Ghadimi, “A new multiobjective allocator of capacitor banks and distributed generations using a new investigated differential evolution,” Complexity, vol. 19, pp. 40-54, 2014.
[44] N. Razmjooy and M. Ramezani, “An Improved Quantum Evolutionary Algorithm Based on Invasive Weed Optimization,” Indian J. Sci. Res, vol. 4, pp. 413-422, 2014.
[45] N. Razmjooy, M. Ramezani, and N. Ghadimi, “Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal,” International Journal of Fuzzy Systems, vol. 19, pp. 1144-1156, 2017.
[46] N. Razmjooy, F. R. Sheykhahmad, and N. Ghadimi, “A hybrid neural network–world cup optimization algorithm for melanoma detection,” Open Medicine, vol. 13, pp. 9-16, 2018.
[47] N. Razmjooy, M. Khalilpour, and M. Ramezani, “A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System,” Journal of Control, Automation and Electrical Systems, vol. 27, pp. 419-440, 2016.
[48] M. A. El Aziz, A. A. Ewees, and A. E. Hassanien, “Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation,” Expert Systems with Applications, vol. 83, pp. 242-256, 2017.
[49] G. Dhiman and V. Kumar, “Emperor penguin optimizer: A bio-inspired algorithm for engineering problems,” Knowledge-Based Systems, vol. 159, pp. 20-50, 2018.
[50] G.-G. Wang, X.-Z. Gao, K. Zenger, and L. d. S. Coelho, “A novel metaheuristic algorithm inspired by rhino herd behavior,” in Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016, 2018, pp. 1026-1033.
[51] J. S. Horne, E. O. Garton, and J. L. Rachlow, “A synoptic model of animal space use: simultaneous estimation of home range, habitat selection, and inter/intra-specific relationships,” Ecological Modelling, vol. 214, pp. 338-348, 2008.
[52] D. Yang, G. Li, and G. Cheng, “On the efficiency of chaos optimization algorithms for global optimization,” Chaos, Solitons & Fractals, vol. 34, pp. 1366-1375, 2007.
[53] C. Rim, S. Piao, G. Li, and U. Pak, “A niching chaos optimization algorithm for multimodal optimization,” Soft Computing, vol. 22, pp. 621-633, 2018.
[54] C. Choi and J.-J. Lee, “Chaotic local search algorithm,” Artificial Life and Robotics, vol. 2, pp. 41-47, 1998.
[55] X. Li, P. Niu, and J. Liu, “Combustion optimization of a boiler based on the chaos and Levy flight vortex search algorithm,” Applied Mathematical Modelling, vol. 58, pp. 3-18, 2018.
[56] O. Abedinia, N. Amjady, and N. Ghadimi, “Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm,” Computational Intelligence, vol. 34, pp. 241-260, 2018.
[57] J. H. Holland, “Genetic algorithms,” Scientific american, vol. 267, pp. 66-73, 1992.
[58] J. C. Bansal, “Particle Swarm Optimization,” in Evolutionary and Swarm Intelligence Algorithms, ed: Springer, 2019, pp. 11-23.
)