Document Type : Original Article
Author
University of Ghana, P.O. Box 134, Legon-Accra, Ghana
10.52293/SE.1.1.364378
Abstract
The present study proposes a new optimal configuration of a combined cooling, heating, and power (CCHP) system for annual dynamic simulation. To provide higher efficiency from the CCHP system, a new enhanced bio-inspired algorithm, namely Amended Coyote Optimizer is designed and utilized. The proposed optimal technique is then carried out to a commercial building in Tongchuan, China. The simulations of the suggested method are finally confirmed by the data achieved by the case study which is done to show the method efficacy. Simulation achievements showed that the suggested technique has better effectiveness to provide similar data with the real value.
Keywords
1. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019.
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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. Zhang, G., et al., Optimal parameter extraction of PEM fuel cells by meta-heuristics. International Journal of Ambient Energy, 2020: p. 1-10.
5. 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.
6. Dehghani, M., et al., Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability, 2020. 13(1): p. 1-1.
7. 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.
8. Eslami, M., et al., A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2018: p. 1-11.
9. Fan, X., et al., High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access, 2020. 8: p. 131975-131987.
10. Firouz, M.H. and Noradin, Gh., Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. Journal of Intelligent & Fuzzy Systems, 2016. 30(2): p. 845-859.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. Yang, Z., et al., Model Parameter Estimation of the PEMFCs Using Improved Barnacles Mating Optimization Algorithm. Energy, 2020: p. 118738.
18. Mahdinia, S., et al., Optimization of PEMFC Model Parameters Using Meta-Heuristics. Sustainability, 2021. 13(22): p. 12771.
19. Li, F., et al., Operation optimization for combined cooling, heating, and power system with condensation heat recovery. Applied Energy, 2018. 230: p. 305-316.
20. Deng, Y., et al., A novel operation strategy based on black hole algorithm to optimize combined cooling, heating, and power-ground source heat pump system. Energy, 2021. 229: p. 120637.
21. Leng, X., et al., Optimal planning of a hybrid system integrating of combined cooling, heat and power and energy storage resources. Sustainable Energy Technologies and Assessments, 2022. 50: p. 101806.
22. Wang, J.-J., Y.-Y. Jing, and C.-F. Zhang, Optimization of capacity and operation for CCHP system by genetic algorithm. Applied Energy, 2010. 87(4): p. 1325-1335.
23. Li, Z., Z. Huo, and H. Yin. Optimization and analysis of operation strategies for combined cooling, heating and power system. in 2011 Asia-Pacific Power and Energy Engineering Conference. 2011. IEEE.
24. 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.
25. 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.
26. 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.
27. Lin, H., C. Yang, and X. Xu, A new optimization model of CCHP system based on genetic algorithm. Sustainable Cities and Society, 2020. 52: p. 101811.
28. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
29. 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.
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. Yuan, Z., et al., Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm. Energy Reports, 2020. 6: p. 1572-1580.
32. Aghajani, G. and N. Ghadimi, Multi-objective energy management in a micro-grid. Energy Reports, 2018. 4: p. 218-225.
33. Ramezani, M., et al., A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home. SN Applied Sciences, 2020. 2(12): p. 1-17.
34. 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.
35. Razmjooy, N., et al., A comprehensive survey of new meta-heuristic algorithms. Recent Advances in Hybrid Metaheuristics for Data Clustering, Wiley Publishing, 2019.
36. Ramezani, M., et al., A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arabian Journal for Science and Engineering, 2021: p. 1-24.
37. Razmjooy, N., M. Ashourian, and Z. Foroozandeh, Metaheuristics and Optimization in Computer and Electrical Engineering. Springer.
38. Simon, D., Biogeography-based optimization. IEEE transactions on evolutionary computation, 2008. 12(6): p. 702-713.
39. Zhao, W., L. Wang, and Z. Zhang, Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access, 2019. 7: p. 73182-73206.
40. Kaveh, A., M. Khanzadi, and M.R. Moghaddam. Billiards-inspired optimization algorithm; a new meta-heuristic method. in Structures. 2020. Elsevier.
41. Pierezan, J. and L.D.S. Coelho. Coyote optimization algorithm: a new metaheuristic for global optimization problems. in 2018 IEEE congress on evolutionary computation (CEC). 2018. IEEE.
42. Kintner-Meyer, M. and A.F. Emery, Cost optimal analysis and load shifting potentials of cold storage equipment. 1995, American Society of Heating, Refrigerating and Air-Conditioning Engineers.
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. 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. Zhang, G., et al., Optimal parameter extraction of PEM fuel cells by meta-heuristics. International Journal of Ambient Energy, 2020: p. 1-10.
5. 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.
6. Dehghani, M., et al., Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability, 2020. 13(1): p. 1-1.
7. 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.
8. Eslami, M., et al., A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2018: p. 1-11.
9. Fan, X., et al., High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access, 2020. 8: p. 131975-131987.
10. Firouz, M.H. and Noradin, Gh., Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. Journal of Intelligent & Fuzzy Systems, 2016. 30(2): p. 845-859.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. Yang, Z., et al., Model Parameter Estimation of the PEMFCs Using Improved Barnacles Mating Optimization Algorithm. Energy, 2020: p. 118738.
18. Mahdinia, S., et al., Optimization of PEMFC Model Parameters Using Meta-Heuristics. Sustainability, 2021. 13(22): p. 12771.
19. Li, F., et al., Operation optimization for combined cooling, heating, and power system with condensation heat recovery. Applied Energy, 2018. 230: p. 305-316.
20. Deng, Y., et al., A novel operation strategy based on black hole algorithm to optimize combined cooling, heating, and power-ground source heat pump system. Energy, 2021. 229: p. 120637.
21. Leng, X., et al., Optimal planning of a hybrid system integrating of combined cooling, heat and power and energy storage resources. Sustainable Energy Technologies and Assessments, 2022. 50: p. 101806.
22. Wang, J.-J., Y.-Y. Jing, and C.-F. Zhang, Optimization of capacity and operation for CCHP system by genetic algorithm. Applied Energy, 2010. 87(4): p. 1325-1335.
23. Li, Z., Z. Huo, and H. Yin. Optimization and analysis of operation strategies for combined cooling, heating and power system. in 2011 Asia-Pacific Power and Energy Engineering Conference. 2011. IEEE.
24. 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.
25. 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.
26. 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.
27. Lin, H., C. Yang, and X. Xu, A new optimization model of CCHP system based on genetic algorithm. Sustainable Cities and Society, 2020. 52: p. 101811.
28. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
29. 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.
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. Yuan, Z., et al., Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm. Energy Reports, 2020. 6: p. 1572-1580.
32. Aghajani, G. and N. Ghadimi, Multi-objective energy management in a micro-grid. Energy Reports, 2018. 4: p. 218-225.
33. Ramezani, M., et al., A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home. SN Applied Sciences, 2020. 2(12): p. 1-17.
34. 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.
35. Razmjooy, N., et al., A comprehensive survey of new meta-heuristic algorithms. Recent Advances in Hybrid Metaheuristics for Data Clustering, Wiley Publishing, 2019.
36. Ramezani, M., et al., A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arabian Journal for Science and Engineering, 2021: p. 1-24.
37. Razmjooy, N., M. Ashourian, and Z. Foroozandeh, Metaheuristics and Optimization in Computer and Electrical Engineering. Springer.
38. Simon, D., Biogeography-based optimization. IEEE transactions on evolutionary computation, 2008. 12(6): p. 702-713.
39. Zhao, W., L. Wang, and Z. Zhang, Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access, 2019. 7: p. 73182-73206.
40. Kaveh, A., M. Khanzadi, and M.R. Moghaddam. Billiards-inspired optimization algorithm; a new meta-heuristic method. in Structures. 2020. Elsevier.
41. Pierezan, J. and L.D.S. Coelho. Coyote optimization algorithm: a new metaheuristic for global optimization problems. in 2018 IEEE congress on evolutionary computation (CEC). 2018. IEEE.
42. Kintner-Meyer, M. and A.F. Emery, Cost optimal analysis and load shifting potentials of cold storage equipment. 1995, American Society of Heating, Refrigerating and Air-Conditioning Engineers.
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