Document Type : Original Article
Author
College of Engineering, University of Miami, Coral Gables, FL 33146, USA
10.52293/SE.1.1.270285
Abstract
The main idea of this paper is to multi-criteria optimal designing of a combined cooling, heat and power (CCHP) system in an industrial unit by considering cooling loads, electricity, and heating. Different scenarios such as selling scenarios, no-selling scenario, as well as the possibility of electricity selling with identical capacities of the gas engine have been utilized. Because of the complexity of this problem, a new developed metaheuristic methodology, called Balanced Tree Growth Algorithm (BTGA) is designed and utilized. Relative Annual Benefit (RAB) as a multi-criterion function along with a gas engine is utilized as the primary mover during the optimization. Final simulation indicate that the proposed approach has well results toward the method from the literature. The results also specified that however using of the proposed configuration gives suitable results for different scenarios, selling scenario is more profitable.
Keywords
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14. Khalilpour, R., et al. Optimal control of DC motor using invasive weed optimization (IWO) algorithm. in Majlesi Conference on Electrical Engineering, Majlesi New Town, Isfahan, Iran. 2011.
15. Meng, Q., et al., A single-phase transformer-less grid-tied inverter based on switched capacitor for PV application. Journal of Control, Automation and Electrical Systems, 2020. 31(1): p. 257-270.
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22. Zhang, G., et al., Optimal parameter extraction of PEM fuel cells by meta-heuristics. International Journal of Ambient Energy, 2020: p. 1-10.
23. Yanda, L., et al., 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.
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28. 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.
29. Mir, M., et al., Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. Computational Intelligence, 2020. 36(1): p. 225-258.
30. Rostamzadeh, M., et al., Optimal location and capacity of multi-distributed generation for loss reduction and voltage profile improvement using imperialist competitive algorithm. Artif. Intell. Research, 2012. 1(2): p. 56-66.
31. 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.
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33. Li, B., et al., Performance analysis and optimization of a CCHP-GSHP coupling system based on quantum genetic algorithm. Sustainable Cities and Society, 2019. 46: p. 101408.
34. Zeng, R., et al., An off-design model to optimize CCHP-GSHP system considering carbon tax. Energy Conversion and Management, 2019. 189: p. 105-117.
35. Zhi, Y., et al., Improved butterfly optimization algorithm for CCHP driven by PEMFC. Applied Thermal Engineering, 2020. 173: p. 114766.
36. Cao, Y., et al., A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm. Energy Reports, 2020. 6: p. 315-324.
37. Ebrahimi, M. and A. Keshavarz, Sizing the prime mover of a residential micro-combined cooling heating and power (CCHP) system by multi-criteria sizing method for different climates. Energy, 2013. 54: p. 291-301.
38. Fei, X., et al., 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.
39. Sanaye, S. and H. Hajabdollahi, 4 E analysis and multi-objective optimization of CCHP using MOPSOA. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2014. 228(1): p. 43-60.
40. Cheraghalipour, A., M. Hajiaghaei-Keshteli, and M.M. Paydar, Tree Growth Algorithm (TGA): A novel approach for solving optimization problems. Engineering Applications of Artificial Intelligence, 2018. 72: p. 393-414.
41. Strumberger, I., et al. Dynamic search tree growth algorithm for global optimization. in Doctoral Conference on Computing, Electrical and Industrial Systems. 2019. Springer.
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. Dhiman, G. and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 2018. 159: p. 20-50.
45. Dhiman, G. and V. Kumar, Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 2017. 114: p. 48-70.
46. Mirjalili, S., S.M. Mirjalili, and A. Hatamlou, Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 2016. 27(2): p. 495-513.
47. Ershadi, H. and A. Karimipour, Present a multi-criteria modeling and optimization (energy, economic and environmental) approach of industrial combined cooling heating and power (CCHP) generation systems using the genetic algorithm, case study: a tile factory. Energy, 2018. 149: p. 286-295.
48. Sanaye, S. and N. Khakpaay, Simultaneous use of MRM (maximum rectangle method) and optimization methods in determining nominal capacity of gas engines in CCHP (combined cooling, heating and power) systems. Energy, 2014. 72: p. 145-158.
2. 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.
3. Cai, W., et al., Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach. Renewable Energy, 2019. 143: p. 1-8.
4. Fathi, G., et al., Stochastic-based energy management of DC microgrids, in Risk-based Energy Management. 2020, Elsevier. p. 31-47.
5. Gao, W., et al., Different states of multi-block based forecast engine for price and load prediction. International Journal of Electrical Power & Energy Systems, 2019. 104: p. 423-435.
6. Ghadimi, N., et al., A new prediction model based on multi-block forecast engine in smart grid. Journal of Ambient Intelligence and Humanized Computing, 2018. 9(6): p. 1873-1888.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. Hosseini, H., et al., A novel method using imperialist competitive algorithm (ICA) for controlling pitch angle in hybrid wind and PV array energy production system. International Journal on Technical and Physical Problems of Engineering (IJTPE), 2012. 11: p. 145-152.
14. Khalilpour, R., et al. Optimal control of DC motor using invasive weed optimization (IWO) algorithm. in Majlesi Conference on Electrical Engineering, Majlesi New Town, Isfahan, Iran. 2011.
15. Meng, Q., et al., A single-phase transformer-less grid-tied inverter based on switched capacitor for PV application. Journal of Control, Automation and Electrical Systems, 2020. 31(1): p. 257-270.
16. Mir, M., et al., Robust optimization-based energy management of hybrid AC/DC microgrids, in Risk-based Energy Management. 2020, Elsevier. p. 229-250.
17. 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.
18. 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.
19. Razmjooy, N., F.R. Sheykhahmad, and N. Ghadimi, A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Medicine, 2018. 13(1): p. 9-16.
20. Zhang, G., et al., Optimal Parameter Extraction of PEM Fuel Cells by Meta-heuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-22.
21. 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.
22. Zhang, G., et al., Optimal parameter extraction of PEM fuel cells by meta-heuristics. International Journal of Ambient Energy, 2020: p. 1-10.
23. Yanda, L., et al., 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.
24. 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).
25. 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.
26. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
27. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019. 5: p. 1551-1559.
28. 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.
29. Mir, M., et al., Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. Computational Intelligence, 2020. 36(1): p. 225-258.
30. Rostamzadeh, M., et al., Optimal location and capacity of multi-distributed generation for loss reduction and voltage profile improvement using imperialist competitive algorithm. Artif. Intell. Research, 2012. 1(2): p. 56-66.
31. 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.
32. Ebrahimi, M. and E. Derakhshan, Design and evaluation of a micro combined cooling, heating, and power system based on polymer exchange membrane fuel cell and thermoelectric cooler. Energy conversion and management, 2018. 171: p. 507-517.
33. Li, B., et al., Performance analysis and optimization of a CCHP-GSHP coupling system based on quantum genetic algorithm. Sustainable Cities and Society, 2019. 46: p. 101408.
34. Zeng, R., et al., An off-design model to optimize CCHP-GSHP system considering carbon tax. Energy Conversion and Management, 2019. 189: p. 105-117.
35. Zhi, Y., et al., Improved butterfly optimization algorithm for CCHP driven by PEMFC. Applied Thermal Engineering, 2020. 173: p. 114766.
36. Cao, Y., et al., A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm. Energy Reports, 2020. 6: p. 315-324.
37. Ebrahimi, M. and A. Keshavarz, Sizing the prime mover of a residential micro-combined cooling heating and power (CCHP) system by multi-criteria sizing method for different climates. Energy, 2013. 54: p. 291-301.
38. Fei, X., et al., 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.
39. Sanaye, S. and H. Hajabdollahi, 4 E analysis and multi-objective optimization of CCHP using MOPSOA. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2014. 228(1): p. 43-60.
40. Cheraghalipour, A., M. Hajiaghaei-Keshteli, and M.M. Paydar, Tree Growth Algorithm (TGA): A novel approach for solving optimization problems. Engineering Applications of Artificial Intelligence, 2018. 72: p. 393-414.
41. Strumberger, I., et al. Dynamic search tree growth algorithm for global optimization. in Doctoral Conference on Computing, Electrical and Industrial Systems. 2019. Springer.
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. Dhiman, G. and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 2018. 159: p. 20-50.
45. Dhiman, G. and V. Kumar, Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 2017. 114: p. 48-70.
46. Mirjalili, S., S.M. Mirjalili, and A. Hatamlou, Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 2016. 27(2): p. 495-513.
47. Ershadi, H. and A. Karimipour, Present a multi-criteria modeling and optimization (energy, economic and environmental) approach of industrial combined cooling heating and power (CCHP) generation systems using the genetic algorithm, case study: a tile factory. Energy, 2018. 149: p. 286-295.
48. Sanaye, S. and N. Khakpaay, Simultaneous use of MRM (maximum rectangle method) and optimization methods in determining nominal capacity of gas engines in CCHP (combined cooling, heating and power) systems. Energy, 2014. 72: p. 145-158.
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