Document Type : Original Article
Author
Islamic Azad University, Science and Research Branch, Tehran, Iran
10.52293/SE.1.1.208225
Abstract
In this study, economic, environmental, and technical optimization of a hybrid Microturbine-Solid Oxide Fuel Cell (SOFC) system is performed in a full load for the distributed generated electricity. To achieve better results, a new modified metaheuristic, called Balanced Manta Ray Foraging Optimization Algorithm is adopted for multi-objective optimization of the problem. The system has been thermodynamically modeled and the results validated the system efficiency by considering the available data from the reference. During the optimization, the decision variable values have been evaluated by considering the system constraints to achieve an optimal criterion for the cost and exergy efficiency objective functions. Also, the cost of environmental degradation penalty has been added to the system total cost. The effect of the fuel price, investment cost, and the system output power value on the system are taken into consideration. The results show that the most sensitive and the most significant design parameter of the system is the current density of the fuel cell where the accurate selection of it, has a big effect on forming a trade-off between the system cost and the efficiency.
Keywords
1. Aghajani, G. and N. Ghadimi, Multi-objective energy management in a micro-grid. Energy Reports, 2018. 4: p. 218-225.
2. Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
3. Alizadeh, E., et al., Investigation of contact pressure distribution over the active area of PEM fuel cell stack. International Journal of Hydrogen Energy, 2016. 41(4): p. 3062-3071.
4. Tian, M.-W., et al., 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, 2020. 249: p. 119414.
5. Fei, X., 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, 2019: p. 1-21.
6. Mirzapour, F., et al., A new prediction model of battery and wind-solar output in hybrid power system. Journal of Ambient Intelligence and Humanized Computing, 2019. 10(1): p. 77-87.
7. Nejad, H.C., et al., Reliability based optimal allocation of distributed generations in transmission systems under demand response program. Electric Power Systems Research, 2019. 176: p. 105952.
8. Shamel, A. and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. 2016.
9. Gollou, A.R. and N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems, 2017. 32(6): p. 4031-4045.
10. Hamian, M., et al., A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Engineering Applications of Artificial Intelligence, 2018. 72: p. 203-212.
11. Fan, X., et al., Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports, 2020. 6: p. 325-335.
12. Yanda, L., Z. Yuwei, and N. Razmjooy, Optimal Arrangement of a Micro-CHP System in the Presence of Fuel Cell-Heat Pump based on Metaheuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-24.
13. Payne, R., J. Love, and M. Kah, Generating electricity at 60% electrical efficiency from 1-2 kWe SOFC products. ECS Transactions, 2009. 25(2): p. 231.
14. Rossi, I., A. Traverso, and D. Tucker, SOFC/Gas Turbine Hybrid System: A simplified framework for dynamic simulation. Applied energy, 2019. 238: p. 1543-1550.
15. Ehyaei, M.A. and M.A. Rosen, Optimization of a triple cycle based on a solid oxide fuel cell and gas and steam cycles with a multiobjective genetic algorithm and energy, exergy and economic analyses. Energy conversion and management, 2019. 180: p. 689-708.
16. Habibollahzade, A., E. Gholamian, and A. Behzadi, Multi-objective optimization and comparative performance analysis of hybrid biomass-based solid oxide fuel cell/solid oxide electrolyzer cell/gas turbine using different gasification agents. Applied Energy, 2019. 233: p. 985-1002.
17. Choudhary, T. and M.K. Sahu, Energy and exergy analysis of solid oxide fuel cell integrated with gas turbine cycle—“A Hybrid Cycle”, in Renewable Energy and its Innovative Technologies. 2019, Springer. p. 139-153.
18. Ding, X., X. Lv, and Y. Weng, Coupling effect of operating parameters on performance of a biogas-fueled solid oxide fuel cell/gas turbine hybrid system. Applied Energy, 2019. 254: p. 113675.
19. Behzadi, A., et al., Multi-objective optimization of a hybrid biomass-based SOFC/GT/double effect absorption chiller/RO desalination system with CO2 recycle. Energy conversion and management, 2019. 181: p. 302-318.
20. Wang, X., X. Lv, and Y. Weng, Performance analysis of a biogas-fueled SOFC/GT hybrid system integrated with anode-combustor exhaust gas recirculation loops. Energy, 2020. 197: p. 117213.
21. Gong, W. and N. razmjooy, A new optimisation algorithm based on OCM and PCM solution through energy reserve. International Journal of Ambient Energy, 2020: p. 1-14.
22. Yin, Z. and N. Razmjooy, PEMFC identification using deep learning developed by improved deer hunting optimization algorithm. International Journal of Power and Energy Systems, 2020. 40(2).
23. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
24. Holman, J.P., Heat transfer. 2002: McGraw-Hill Science, Engineering & Mathematics.
25. Sanaye, S. and H. Hajabdollahi, Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm. Applied Energy, 2010. 87(6): p. 1893-1902.
26. Bavarsad, P.G., Energy and exergy analysis of internal reforming solid oxide fuel cell–gas turbine hybrid system. International journal of hydrogen energy, 2007. 32(17): p. 4591-4599.
27. Chan, S., H. Ho, and Y. Tian, Modelling of simple hybrid solid oxide fuel cell and gas turbine power plant. Journal of power sources, 2002. 109(1): p. 111-120.
28. Zhang, G., C. Xiao, and N. Razmjooy, Optimal Parameter Extraction of PEM Fuel Cells by Meta-heuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-22.
29. Guo, Y., et al., An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 2020. 6: p. 885-894.
30. Yuan, Z., et al., A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm. Energy Reports, 2020. 6: p. 662-671.
31. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019. 5: p. 1551-1559.
32. Yu, D., et al., System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 2019. 5: p. 1365-1374.
33. Cuneo, A., et al., Gas turbine size optimization in a hybrid system considering SOFC degradation. Applied Energy, 2018. 230: p. 855-864.
34. Arsalis, A. and G.E. Georghiou, Thermoeconomic Optimization of a Hybrid Photovoltaic-Solid Oxide Fuel Cell System for Decentralized Application. Applied Sciences, 2019. 9(24): p. 5450.
35. Hajilounezhad, T., S. Safari, and M. Aliehyaei, Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms. 2020.
36. Hosseini Firouz, M. and N. Ghadimi, Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods. Complexity, 2016. 21(6): p. 70-88.
37. Leng, H., et al., A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting. Advanced Engineering Informatics, 2018. 36: p. 20-30.
38. Liu, Y., W. Wang, and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
39. Lazzaretto, A. and A. Toffolo, Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design. Energy, 2004. 29(8): p. 1139-1157.
40. Calise, F., et al., Single-level optimization of a hybrid SOFC–GT power plant. Journal of Power Sources, 2006. 159(2): p. 1169-1185.
41. Zhao, W., Z. Zhang, and L. Wang, Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 2020. 87: p. 103300.
42. Yang, D., G. Li, and G. Cheng, On the efficiency of chaos optimization algorithms for global optimization. Chaos, Solitons & Fractals, 2007. 34(4): p. 1366-1375.
43. Rim, C., et al., A niching chaos optimization algorithm for multimodal optimization. Soft Computing, 2018. 22(2): p. 621-633.
44. Rashedi, E., H. Nezamabadi-Pour, and S. Saryazdi, GSA: a gravitational search algorithm. Information sciences, 2009. 179(13): p. 2232-2248.
45. Gandomi, A.H. and A.H. Alavi, Krill herd: a new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 2012. 17(12): p. 4831-4845.
46. Askarzadeh, A., A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures, 2016. 169: p. 1-12.
47. Yapici, H. and N. Cetinkaya, A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 2019. 78: p. 545-568.
48. Campanari, S., Full load and part-load performance prediction for integrated SOFC and microturbine systems. J. Eng. Gas Turbines Power, 2000. 122(2): p. 239-246.
49. Leal, E.M., L.A. Bortolaia, and A.M.L. Junior, Technical analysis of a hybrid solid oxide fuel cell/gas turbine cycle. Energy Conversion and Management, 2019. 202: p. 112195.
50. Abrassi, A., et al., Impact of Different Volume Sizes on Dynamic Stability of a Gas Turbine-Fuel Cell Hybrid System. Journal of Engineering for Gas Turbines and Power, 2020. 142(5).
51. Akroot, A., L. Namli, and H. Ozcan, Compared Thermal Modeling of Anode-and Electrolyte-Supported SOFC-Gas Turbine Hybrid Systems. Journal of Electrochemical Energy Conversion and Storage, 2020. 18(1).
52. Barzegar Avval, H., et al., Thermo‐economic‐environmental multiobjective optimization of a gas turbine power plant with preheater using evolutionary algorithm. International Journal of Energy Research, 2011. 35(5): p. 389-403.
2. Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
3. Alizadeh, E., et al., Investigation of contact pressure distribution over the active area of PEM fuel cell stack. International Journal of Hydrogen Energy, 2016. 41(4): p. 3062-3071.
4. Tian, M.-W., et al., 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, 2020. 249: p. 119414.
5. Fei, X., 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, 2019: p. 1-21.
6. Mirzapour, F., et al., A new prediction model of battery and wind-solar output in hybrid power system. Journal of Ambient Intelligence and Humanized Computing, 2019. 10(1): p. 77-87.
7. Nejad, H.C., et al., Reliability based optimal allocation of distributed generations in transmission systems under demand response program. Electric Power Systems Research, 2019. 176: p. 105952.
8. Shamel, A. and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. 2016.
9. Gollou, A.R. and N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems, 2017. 32(6): p. 4031-4045.
10. Hamian, M., et al., A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Engineering Applications of Artificial Intelligence, 2018. 72: p. 203-212.
11. Fan, X., et al., Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports, 2020. 6: p. 325-335.
12. Yanda, L., Z. Yuwei, and N. Razmjooy, Optimal Arrangement of a Micro-CHP System in the Presence of Fuel Cell-Heat Pump based on Metaheuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-24.
13. Payne, R., J. Love, and M. Kah, Generating electricity at 60% electrical efficiency from 1-2 kWe SOFC products. ECS Transactions, 2009. 25(2): p. 231.
14. Rossi, I., A. Traverso, and D. Tucker, SOFC/Gas Turbine Hybrid System: A simplified framework for dynamic simulation. Applied energy, 2019. 238: p. 1543-1550.
15. Ehyaei, M.A. and M.A. Rosen, Optimization of a triple cycle based on a solid oxide fuel cell and gas and steam cycles with a multiobjective genetic algorithm and energy, exergy and economic analyses. Energy conversion and management, 2019. 180: p. 689-708.
16. Habibollahzade, A., E. Gholamian, and A. Behzadi, Multi-objective optimization and comparative performance analysis of hybrid biomass-based solid oxide fuel cell/solid oxide electrolyzer cell/gas turbine using different gasification agents. Applied Energy, 2019. 233: p. 985-1002.
17. Choudhary, T. and M.K. Sahu, Energy and exergy analysis of solid oxide fuel cell integrated with gas turbine cycle—“A Hybrid Cycle”, in Renewable Energy and its Innovative Technologies. 2019, Springer. p. 139-153.
18. Ding, X., X. Lv, and Y. Weng, Coupling effect of operating parameters on performance of a biogas-fueled solid oxide fuel cell/gas turbine hybrid system. Applied Energy, 2019. 254: p. 113675.
19. Behzadi, A., et al., Multi-objective optimization of a hybrid biomass-based SOFC/GT/double effect absorption chiller/RO desalination system with CO2 recycle. Energy conversion and management, 2019. 181: p. 302-318.
20. Wang, X., X. Lv, and Y. Weng, Performance analysis of a biogas-fueled SOFC/GT hybrid system integrated with anode-combustor exhaust gas recirculation loops. Energy, 2020. 197: p. 117213.
21. Gong, W. and N. razmjooy, A new optimisation algorithm based on OCM and PCM solution through energy reserve. International Journal of Ambient Energy, 2020: p. 1-14.
22. Yin, Z. and N. Razmjooy, PEMFC identification using deep learning developed by improved deer hunting optimization algorithm. International Journal of Power and Energy Systems, 2020. 40(2).
23. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
24. Holman, J.P., Heat transfer. 2002: McGraw-Hill Science, Engineering & Mathematics.
25. Sanaye, S. and H. Hajabdollahi, Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm. Applied Energy, 2010. 87(6): p. 1893-1902.
26. Bavarsad, P.G., Energy and exergy analysis of internal reforming solid oxide fuel cell–gas turbine hybrid system. International journal of hydrogen energy, 2007. 32(17): p. 4591-4599.
27. Chan, S., H. Ho, and Y. Tian, Modelling of simple hybrid solid oxide fuel cell and gas turbine power plant. Journal of power sources, 2002. 109(1): p. 111-120.
28. Zhang, G., C. Xiao, and N. Razmjooy, Optimal Parameter Extraction of PEM Fuel Cells by Meta-heuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-22.
29. Guo, Y., et al., An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 2020. 6: p. 885-894.
30. Yuan, Z., et al., A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm. Energy Reports, 2020. 6: p. 662-671.
31. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019. 5: p. 1551-1559.
32. Yu, D., et al., System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 2019. 5: p. 1365-1374.
33. Cuneo, A., et al., Gas turbine size optimization in a hybrid system considering SOFC degradation. Applied Energy, 2018. 230: p. 855-864.
34. Arsalis, A. and G.E. Georghiou, Thermoeconomic Optimization of a Hybrid Photovoltaic-Solid Oxide Fuel Cell System for Decentralized Application. Applied Sciences, 2019. 9(24): p. 5450.
35. Hajilounezhad, T., S. Safari, and M. Aliehyaei, Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms. 2020.
36. Hosseini Firouz, M. and N. Ghadimi, Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods. Complexity, 2016. 21(6): p. 70-88.
37. Leng, H., et al., A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting. Advanced Engineering Informatics, 2018. 36: p. 20-30.
38. Liu, Y., W. Wang, and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
39. Lazzaretto, A. and A. Toffolo, Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design. Energy, 2004. 29(8): p. 1139-1157.
40. Calise, F., et al., Single-level optimization of a hybrid SOFC–GT power plant. Journal of Power Sources, 2006. 159(2): p. 1169-1185.
41. Zhao, W., Z. Zhang, and L. Wang, Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 2020. 87: p. 103300.
42. Yang, D., G. Li, and G. Cheng, On the efficiency of chaos optimization algorithms for global optimization. Chaos, Solitons & Fractals, 2007. 34(4): p. 1366-1375.
43. Rim, C., et al., A niching chaos optimization algorithm for multimodal optimization. Soft Computing, 2018. 22(2): p. 621-633.
44. Rashedi, E., H. Nezamabadi-Pour, and S. Saryazdi, GSA: a gravitational search algorithm. Information sciences, 2009. 179(13): p. 2232-2248.
45. Gandomi, A.H. and A.H. Alavi, Krill herd: a new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 2012. 17(12): p. 4831-4845.
46. Askarzadeh, A., A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures, 2016. 169: p. 1-12.
47. Yapici, H. and N. Cetinkaya, A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 2019. 78: p. 545-568.
48. Campanari, S., Full load and part-load performance prediction for integrated SOFC and microturbine systems. J. Eng. Gas Turbines Power, 2000. 122(2): p. 239-246.
49. Leal, E.M., L.A. Bortolaia, and A.M.L. Junior, Technical analysis of a hybrid solid oxide fuel cell/gas turbine cycle. Energy Conversion and Management, 2019. 202: p. 112195.
50. Abrassi, A., et al., Impact of Different Volume Sizes on Dynamic Stability of a Gas Turbine-Fuel Cell Hybrid System. Journal of Engineering for Gas Turbines and Power, 2020. 142(5).
51. Akroot, A., L. Namli, and H. Ozcan, Compared Thermal Modeling of Anode-and Electrolyte-Supported SOFC-Gas Turbine Hybrid Systems. Journal of Electrochemical Energy Conversion and Storage, 2020. 18(1).
52. Barzegar Avval, H., et al., Thermo‐economic‐environmental multiobjective optimization of a gas turbine power plant with preheater using evolutionary algorithm. International Journal of Energy Research, 2011. 35(5): p. 389-403.
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