Document Type : Original Article
Author
Department of Engineering, Texas Tech University, Lubbock, TX 79409, USA
10.52293/SE.1.1.303318
Abstract
A new technique is prsented in this paper for optimum design of a combined cooling heating and power (CCHP) system. The prime mover in this study is a gas turbine and this is designed for a rural area in Zhaoping County, Guangxi, China. The effectiveness of the method is assessed based on four main parameters: exergetic, energetic, economic, and environmental features. A Modified Group Teaching Optimization Algorithm (MGTO)is utilized for achieving resuls with better accuracy and convergence. The achievements of the suggested MGTO-based technique are put in comparison with genetic optimizer-based method and improved owl optimization algorithm-based method to illustrate the method higher efficiency.
Keywords
1. Ghadimi, N., An adaptive neuro‐fuzzy inference system for islanding detection in wind turbine as distributed generation. Complexity, 2015. 21(1): p. 10-20.
2. Ghiasi, M. , et al., An analytical methodology for reliability assessment and failure analysis in distributed power system. SN Applied Sciences, 2019. 1(1): p. 44.
3. Mir, M., et al., Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction. Evolving Systems, 2020. 11(4): p. 559-573.
4. Ebrahimian, H., et al., The price prediction for the energy market based on a new method. Economic research-Ekonomska istraživanja, 2018. 31(1): p. 313-337.
5. Liu, Y. , et al., Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
6. Gheydi, M. , et al., Planning in microgrids with conservation of voltage reduction. IEEE Systems Journal, 2016. 12(3): p. 2782-2790.
7. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
8. Zhi, Y., et al., New approaches for regulation of solid oxide fuel cell using dynamic condition approximation and STATCOM. International Transactions on Electrical Energy Systems: p. e12756.
9. 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.
10. Yu, D., et al., Energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique. Journal of Energy Storage, 2020. 27: p. 101054.
11. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019.
12. Ye, H., et al., High step-up interleaved dc/dc converter with high efficiency. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: p. 1-20.
13. Fan, X., et al., High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access, 2020. 8: p. 131975-131987.
14. Khodaei, H., et al., Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Applied Thermal Engineering, 2018. 137: p. 395-405.
15. Hagh, M.T. , et al., Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG. Frontiers in Energy, 2015. 9(1): p. 75-90.
16. 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: p. 1-12.
17. Yuan, Z., et al., Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Generation, Transmission & Distribution, 2020. 14(17): p. 3478-3487.
18. Yu, D. and N. Ghadimi, Reliability constraint stochastic UC by considering the correlation of random variables with Copula theory. IET Renewable Power Generation, 2019. 13(14): p. 2587-2593.
19. 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.
20. 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.
21. Wang, Z., et al., A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study. International Transactions on Electrical Energy Systems: p. e12806.
22. Wang, J., et al., Optimal design of hybrid combined cooling, heating and power systems considering the uncertainties of load demands and renewable energy sources. Journal of Cleaner Production, 2021. 281: p. 125357.
23. Chen, K. and M. Pan, Operation optimization of combined cooling, heating, and power superstructure system for satisfying demand fluctuation. Energy, 2021. 237: p. 121599.
24. Li, L.-L., et al., Performance assessment of combined cooling, heating and power system operation strategy based on multi-objective seagull optimization algorithm. Energy Conversion and Management, 2021. 244: p. 114443.
25. Mahian, O., et al., Optimal sizing and performance assessment of a hybrid combined heat and power system with energy storage for residential buildings. Energy Conversion and Management, 2020. 211: p. 112751.
26. Farahnak, M., et al., Optimal sizing of power generation unit capacity in ICE-driven CCHP systems for various residential building sizes. Applied Energy, 2015. 158: p. 203-219.
27. Mueggler, E., The age of wild ghosts: memory, violence, and place in Southwest China. 2001: Univ of California Press.
28. 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.
29. 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.
30. Atigh, H.S.Q., M. Deymi‐Dashtebayaz, and A. Bak‐Khoshnevis, Optimal sizing of gas engine in combined cooling heat and power systems by a proposed evaluation function and genetic algorithm optimization methods based on 4E analysis. Environmental Progress & Sustainable Energy, 2019.
31. Fumo, N. and L.M. Chamra, Analysis of combined cooling, heating, and power systems based on source primary energy consumption. Applied Energy, 2010. 87(6): p. 2023-2030.
32. Tataraki, K.G., K.C. Kavvadias, and Z.B. Maroulis, A systematic approach to evaluate the economic viability of Combined Cooling Heating and Power systems over conventional technologies. Energy, 2018. 148: p. 283-295.
33. Ebrahimi, M. and A. Keshavarz, Combined cooling, heating and power: decision-making, design and optimization. 2014: Elsevier.
34. Ghadimi, P., S. Kara, and B. Kornfeld, The optimal selection of on-site CHP systems through integrated sizing and operational strategy. Applied Energy, 2014. 126: p. 38-46.
35. Javan, S., et al., Fluid selection optimization of a combined cooling, heating and power (CCHP) system for residential applications. Applied Thermal Engineering, 2016. 96: p. 26-38.
36. Sanaye, S., M.M. Ghafurian, and F.T. Dastjerd, Applying Relative Net Present or Relative Net Future Worth Benefit and exergy efficiency for optimum selection of a natural gas engine based CCHP system for a hotel building. Journal of Natural Gas Science and Engineering, 2016. 34: p. 305-317.
37. Sanaye, S. and M.M. Ghafurian, Applying relative equivalent uniform annual benefit for optimum selection of a gas engine combined cooling, heating and power system for residential buildings. Energy and Buildings, 2016. 128: p. 809-818.
38. Mago, P.J. and A.K. Hueffed, Evaluation of a turbine driven CCHP system for large office buildings under different operating strategies. Energy and Buildings, 2010. 42(10): p. 1628-1636.
39. UK, G., Government emission conversion factors for greenhouse gas company reporting. 2017.
40. Zhang, Y. and Z. Jin, Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Systems with Applications, 2020. 148: p. 113246.
41. Mehdi Ramezani, et al., A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arabian Journal for Science and Engineering, 2021.
42. Tizhoosh, H.R. Opposition-based learning: a new scheme for machine intelligence. in International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06). 2005. IEEE.
43. 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.
44. Razmjooy, N., et al., A comprehensive survey of new meta-heuristic algorithms. Recent Advances in Hybrid Metaheuristics for Data Clustering, Wiley Publishing, 2019.
45. Razmjooy, N., M. Ashourian, and Z. Foroozandeh, Metaheuristics and Optimization in Computer and Electrical Engineering. Springer.
46. Razmjooy, N., 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, 2016. 27(4): p. 419-440.
47. 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.
48. Rim, C., et al., A niching chaos optimization algorithm for multimodal optimization. Soft Computing, 2018. 22(2): p. 621-633.
49. Li, H., et al., Improvement of energy supply configuration for telecommunication system in remote area s based on improved chaotic world cup optimization algorithm. Energy, 2020. 192: p. 116614.
50. Hatamlou, A., Black hole: A new heuristic optimization approach for data clustering. Information sciences, 2013. 222: p. 175-184.
51. Dhiman, G. and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 2018. 159: p. 20-50.
52. Cuevas, E., F. Fausto, and A. González, The Locust Swarm Optimization Algorithm, in New Advancements in Swarm Algorithms: Operators and Applications. 2020, Springer. p. 139-159.
53. Yang, Z., et al., Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm. Energy, 2020. 212: p. 118738.
54. 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.
55. Saadi‐Quchan Atigh, H., M. Deymi‐Dashtebayaz, and A. Bak‐Khoshnevis, Optimal sizing of gas engine in combined cooling heat and power systems by a proposed evaluation function and genetic algorithm optimization methods based on 4E analysis. Environmental Progress & Sustainable Energy, 2019. 38(5): p. 13202.
56. Cao, Z., D. Kui, and M. Ashourian, Improved owl search algorithm for optimal capacity determination of the gas engine in a CCHP system using 4E analysis. International Transactions on Electrical Energy Systems, 2020. 30(10): p. e12552.
2. Ghiasi, M. , et al., An analytical methodology for reliability assessment and failure analysis in distributed power system. SN Applied Sciences, 2019. 1(1): p. 44.
3. Mir, M., et al., Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction. Evolving Systems, 2020. 11(4): p. 559-573.
4. Ebrahimian, H., et al., The price prediction for the energy market based on a new method. Economic research-Ekonomska istraživanja, 2018. 31(1): p. 313-337.
5. Liu, Y. , et al., Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
6. Gheydi, M. , et al., Planning in microgrids with conservation of voltage reduction. IEEE Systems Journal, 2016. 12(3): p. 2782-2790.
7. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
8. Zhi, Y., et al., New approaches for regulation of solid oxide fuel cell using dynamic condition approximation and STATCOM. International Transactions on Electrical Energy Systems: p. e12756.
9. 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.
10. Yu, D., et al., Energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique. Journal of Energy Storage, 2020. 27: p. 101054.
11. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019.
12. Ye, H., et al., High step-up interleaved dc/dc converter with high efficiency. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: p. 1-20.
13. Fan, X., et al., High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access, 2020. 8: p. 131975-131987.
14. Khodaei, H., et al., Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Applied Thermal Engineering, 2018. 137: p. 395-405.
15. Hagh, M.T. , et al., Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG. Frontiers in Energy, 2015. 9(1): p. 75-90.
16. 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: p. 1-12.
17. Yuan, Z., et al., Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Generation, Transmission & Distribution, 2020. 14(17): p. 3478-3487.
18. Yu, D. and N. Ghadimi, Reliability constraint stochastic UC by considering the correlation of random variables with Copula theory. IET Renewable Power Generation, 2019. 13(14): p. 2587-2593.
19. 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.
20. 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.
21. Wang, Z., et al., A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study. International Transactions on Electrical Energy Systems: p. e12806.
22. Wang, J., et al., Optimal design of hybrid combined cooling, heating and power systems considering the uncertainties of load demands and renewable energy sources. Journal of Cleaner Production, 2021. 281: p. 125357.
23. Chen, K. and M. Pan, Operation optimization of combined cooling, heating, and power superstructure system for satisfying demand fluctuation. Energy, 2021. 237: p. 121599.
24. Li, L.-L., et al., Performance assessment of combined cooling, heating and power system operation strategy based on multi-objective seagull optimization algorithm. Energy Conversion and Management, 2021. 244: p. 114443.
25. Mahian, O., et al., Optimal sizing and performance assessment of a hybrid combined heat and power system with energy storage for residential buildings. Energy Conversion and Management, 2020. 211: p. 112751.
26. Farahnak, M., et al., Optimal sizing of power generation unit capacity in ICE-driven CCHP systems for various residential building sizes. Applied Energy, 2015. 158: p. 203-219.
27. Mueggler, E., The age of wild ghosts: memory, violence, and place in Southwest China. 2001: Univ of California Press.
28. 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.
29. 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.
30. Atigh, H.S.Q., M. Deymi‐Dashtebayaz, and A. Bak‐Khoshnevis, Optimal sizing of gas engine in combined cooling heat and power systems by a proposed evaluation function and genetic algorithm optimization methods based on 4E analysis. Environmental Progress & Sustainable Energy, 2019.
31. Fumo, N. and L.M. Chamra, Analysis of combined cooling, heating, and power systems based on source primary energy consumption. Applied Energy, 2010. 87(6): p. 2023-2030.
32. Tataraki, K.G., K.C. Kavvadias, and Z.B. Maroulis, A systematic approach to evaluate the economic viability of Combined Cooling Heating and Power systems over conventional technologies. Energy, 2018. 148: p. 283-295.
33. Ebrahimi, M. and A. Keshavarz, Combined cooling, heating and power: decision-making, design and optimization. 2014: Elsevier.
34. Ghadimi, P., S. Kara, and B. Kornfeld, The optimal selection of on-site CHP systems through integrated sizing and operational strategy. Applied Energy, 2014. 126: p. 38-46.
35. Javan, S., et al., Fluid selection optimization of a combined cooling, heating and power (CCHP) system for residential applications. Applied Thermal Engineering, 2016. 96: p. 26-38.
36. Sanaye, S., M.M. Ghafurian, and F.T. Dastjerd, Applying Relative Net Present or Relative Net Future Worth Benefit and exergy efficiency for optimum selection of a natural gas engine based CCHP system for a hotel building. Journal of Natural Gas Science and Engineering, 2016. 34: p. 305-317.
37. Sanaye, S. and M.M. Ghafurian, Applying relative equivalent uniform annual benefit for optimum selection of a gas engine combined cooling, heating and power system for residential buildings. Energy and Buildings, 2016. 128: p. 809-818.
38. Mago, P.J. and A.K. Hueffed, Evaluation of a turbine driven CCHP system for large office buildings under different operating strategies. Energy and Buildings, 2010. 42(10): p. 1628-1636.
39. UK, G., Government emission conversion factors for greenhouse gas company reporting. 2017.
40. Zhang, Y. and Z. Jin, Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Systems with Applications, 2020. 148: p. 113246.
41. Mehdi Ramezani, et al., A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arabian Journal for Science and Engineering, 2021.
42. Tizhoosh, H.R. Opposition-based learning: a new scheme for machine intelligence. in International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06). 2005. IEEE.
43. 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.
44. Razmjooy, N., et al., A comprehensive survey of new meta-heuristic algorithms. Recent Advances in Hybrid Metaheuristics for Data Clustering, Wiley Publishing, 2019.
45. Razmjooy, N., M. Ashourian, and Z. Foroozandeh, Metaheuristics and Optimization in Computer and Electrical Engineering. Springer.
46. Razmjooy, N., 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, 2016. 27(4): p. 419-440.
47. 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.
48. Rim, C., et al., A niching chaos optimization algorithm for multimodal optimization. Soft Computing, 2018. 22(2): p. 621-633.
49. Li, H., et al., Improvement of energy supply configuration for telecommunication system in remote area s based on improved chaotic world cup optimization algorithm. Energy, 2020. 192: p. 116614.
50. Hatamlou, A., Black hole: A new heuristic optimization approach for data clustering. Information sciences, 2013. 222: p. 175-184.
51. Dhiman, G. and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 2018. 159: p. 20-50.
52. Cuevas, E., F. Fausto, and A. González, The Locust Swarm Optimization Algorithm, in New Advancements in Swarm Algorithms: Operators and Applications. 2020, Springer. p. 139-159.
53. Yang, Z., et al., Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm. Energy, 2020. 212: p. 118738.
54. 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.
55. Saadi‐Quchan Atigh, H., M. Deymi‐Dashtebayaz, and A. Bak‐Khoshnevis, Optimal sizing of gas engine in combined cooling heat and power systems by a proposed evaluation function and genetic algorithm optimization methods based on 4E analysis. Environmental Progress & Sustainable Energy, 2019. 38(5): p. 13202.
56. Cao, Z., D. Kui, and M. Ashourian, Improved owl search algorithm for optimal capacity determination of the gas engine in a CCHP system using 4E analysis. International Transactions on Electrical Energy Systems, 2020. 30(10): p. e12552.
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