Document Type : Original Article
Authors
1 Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran
2 Department of Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
10.52293/SE.1.1.286302
Abstract
The required cold and heat are supplied by recycling the heat lost from the stimulus in combined cooling, heating, and power (CCHP) generation system. The present study proposes a new optimal arrangement for a CCHP system for annual dynamic simulation. This study uses CCHP system to design a separated stand-alone generation system to provide higher effectiveness. The system is then improved by a new enhanced metaheuristic technique, namely supply-demand-based optimization algorithm to enhance the efficiency of the designed system. The method is implemented on a hospital and its achievements are put in comparison with several different newest optimization techniques. The achievements indicate that the electricity system purchased from the utility network and fuel consumption for the optimized combined cooling, heating, and power system in comparison with the system of separated generation provides a decreasing trend.
Keywords
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[10] M. Bagheri, V. Nurmanova, O. Abedinia, M. S. Naderi, M. S. Naderi, and N. Ghadimi, “A novel wind power forecasting based feature selection and hybrid forecast engine bundled with honey bee mating optimization,” in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 2018, pp. 1-6: IEEE.
[11] W. Cai, R. Mohammaditab, G. Fathi, K. Wakil, A. G. Ebadi, and N. Ghadimi, “Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach,” Renewable Energy, vol. 143, pp. 1-8, 2019.
[12] 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.
[13] 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.
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[16] M. Eslami, H. A. Moghadam, H. Zayandehroodi, and N. Ghadimi, “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, pp. 1-11, 2018.
[17] X. Fan, H. Sun, Z. Yuan, Z. Li, R. Shi, and N. Ghadimi, “High voltage gain DC/DC converter using coupled inductor and VM techniques,” IEEE Access, vol. 8, pp. 131975-131987, 2020.
[18] M. H. Firouz and N. Ghadimi, “Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system,” Journal of Intelligent & Fuzzy Systems, vol. 30, no. 2, pp. 845-859, 2016.
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[23] M. Ghiasi, N. Ghadimi, and E. Ahmadinia, “An analytical methodology for reliability assessment and failure analysis in distributed power system,” SN Applied Sciences, vol. 1, no. 1, p. 44, 2019.
[24] A. R. Gollou and N. Ghadimi, “A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets,” Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 4031-4045, 2017.
[25] M. T. Hagh, H. Ebrahimian, and N. Ghadimi, “Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG,” Frontiers in Energy, vol. 9, no. 1, pp. 75-90, 2015.
[26] M. Hamian, A. Darvishan, M. Hosseinzadeh, M. J. Lariche, N. Ghadimi, and A. Nouri, “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, vol. 72, pp. 203-212, 2018.
[27] E. Gholamian, P. Hanafizadeh, P. Ahmadi, and L. Mazzarella, “A transient optimization and techno-economic assessment of a building integrated combined cooling, heating and power system in Tehran,” Energy Conversion and Management, vol. 217, p. 112962, 2020.
[28] C. Lu, J. Wang, and R. Yan, “Multi-objective optimization of combined cooling, heating and power system considering the collaboration of thermal energy storage with load uncertainties,” Journal of Energy Storage, vol. 40, p. 102819, 2021.
[29] A. T. Aghaei and R. K. Saray, “Optimization of a combined cooling, heating, and power (CCHP) system with a gas turbine prime mover: A case study in the dairy industry,” Energy, vol. 229, p. 120788, 2021.
[30] W. Yanan, W. Jiekang, and M. Xiaoming, “Intelligent scheduling optimization of seasonal CCHP system using rolling horizon hybrid optimization algorithm and matrix model framework,” IEEE Access, vol. 6, pp. 75132-75142, 2018.
[31] Z.-X. Wang, Y.-F. Zhao, and L.-Y. He, “Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average,” Applied Soft Computing, p. 106475, 2020.
[32] L. Sun, X.-F. Han, Y.-P. Xu, and N. Razmjooy, “Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm,” Energy Reports, vol. 7, pp. 7424-7433, 2021.
[33] Z. Yuan, W. Wang, H. Wang, and N. Razmjooy, “A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm,” Energy Reports, vol. 6, pp. 662-671, 2020.
[34] N. Razmjooy, M. Ashourian, and Z. Foroozandeh, “Metaheuristics and Optimization in Computer and Electrical Engineering,” ed: Springer.
[35] N. Razmjooy, V. V. Estrela, H. J. Loschi, and W. Fanfan, “A comprehensive survey of new meta-heuristic algorithms,” Recent Advances in Hybrid Metaheuristics for Data Clustering, Wiley Publishing, 2019.
[36] L. Yanda, 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, pp. 1-12, 2020.
[37] D. B. Mehdi Ramezani, Navid Razmjooy, “A New Improved Model of Marine Predator Algorithm for Optimization Problems,” Arabian Journal for Science and Engineering, 2021.
[38] W. Zhao, L. Wang, and Z. Zhang, “Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization,” IEEE Access, vol. 7, pp. 73182-73206, 2019.
[39] 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.
[40] G. Guo and N. Razmjooy, “A new interval differential equation for edge detection and determining breast cancer regions in mammography images,” Systems Science & Control Engineering, vol. 7, no. 1, pp. 346-356, 2019.
[41] R. Mousavifard, M. Abolghasemzadeh, N. Razmjooy, and Y. Alizadeh, “Optimal Design of Functionally Graded Steels Using Multi-objective Ant Lion Optimizer,” 2019.
[42] M. Yazdani and F. Jolai, “Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm,” Journal of computational design and engineering, vol. 3, no. 1, pp. 24-36, 2016.
[43] D. Amali and M. Dinakaran, “Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour,” Journal of Intelligent & Fuzzy Systems, no. Preprint, pp. 1-14, 2019.
[44] S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in engineering software, vol. 95, pp. 51-67, 2016.
[45] G. Zhang, C. Xiao, and N. Razmjooy, “Optimal parameter extraction of PEM fuel cells by meta-heuristics,” International Journal of Ambient Energy, pp. 1-10, 2020.
[46] Y. Cao, Q. Wang, Z. Wang, K. Jermsittiparsert, and M. Shafiee, “A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm,” Energy Reports, vol. 6, pp. 315-324, 2020.
[47] L. Zhao, Z. Li, H. Chen, J. Li, J. Xiao, and N. Yousefi, “A multi-criteria optimization for a CCHP with the fuel cell as primary mover using modified Harris Hawks optimization,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1-16, 2020.
[2] X. Chen et al., “Multi-criteria assessment and optimization study on 5 kW PEMFC based residential CCHP system,” Energy conversion and management, vol. 160, pp. 384-395, 2018.
[3] X. Guo, X. Yan, and K. Jermsittiparsert, “Using the modified mayfly algorithm for optimizing the component size and operation strategy of a high temperature PEMFC-powered CCHP,” Energy Reports, vol. 7, pp. 1234-1245, 2021.
[4] H. Lin, C. Yang, and X. Xu, “A new optimization model of CCHP system based on genetic algorithm,” Sustainable Cities and Society, vol. 52, p. 101811, 2020.
[5] F. Ren, Z. Wei, and X. Zhai, “Multi-objective optimization and evaluation of hybrid CCHP systems for different building types,” Energy, vol. 215, p. 119096, 2021.
[6] J.-J. Wang, Y.-Y. Jing, and C.-F. Zhang, “Optimization of capacity and operation for CCHP system by genetic algorithm,” Applied Energy, vol. 87, no. 4, pp. 1325-1335, 2010.
[7] S. Mahdinia, M. Rezaie, M. Elveny, N. Ghadimi, and N. Razmjooy, “Optimization of PEMFC Model Parameters Using Meta-Heuristics,” Sustainability, vol. 13, no. 22, p. 12771, 2021.
[8] G. Aghajani and N. Ghadimi, “Multi-objective energy management in a micro-grid,” Energy Reports, vol. 4, pp. 218-225, 2018.
[9] P. Akbary, M. Ghiasi, M. R. R. Pourkheranjani, H. Alipour, and N. Ghadimi, “Extracting appropriate nodal marginal prices for all types of committed reserve,” Computational Economics, vol. 53, no. 1, pp. 1-26, 2019.
[10] M. Bagheri, V. Nurmanova, O. Abedinia, M. S. Naderi, M. S. Naderi, and N. Ghadimi, “A novel wind power forecasting based feature selection and hybrid forecast engine bundled with honey bee mating optimization,” in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 2018, pp. 1-6: IEEE.
[11] W. Cai, R. Mohammaditab, G. Fathi, K. Wakil, A. G. Ebadi, and N. Ghadimi, “Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach,” Renewable Energy, vol. 143, pp. 1-8, 2019.
[12] 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.
[13] 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.
[14] M. Dehghani et al., “Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare,” Sustainability, vol. 13, no. 1, pp. 1-1, 2020.
[15] H. Ebrahimian, S. Barmayoon, M. Mohammadi, and N. Ghadimi, “The price prediction for the energy market based on a new method,” Economic research-Ekonomska istraživanja, vol. 31, no. 1, pp. 313-337, 2018.
[16] M. Eslami, H. A. Moghadam, H. Zayandehroodi, and N. Ghadimi, “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, pp. 1-11, 2018.
[17] X. Fan, H. Sun, Z. Yuan, Z. Li, R. Shi, and N. Ghadimi, “High voltage gain DC/DC converter using coupled inductor and VM techniques,” IEEE Access, vol. 8, pp. 131975-131987, 2020.
[18] M. H. Firouz and N. Ghadimi, “Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system,” Journal of Intelligent & Fuzzy Systems, vol. 30, no. 2, pp. 845-859, 2016.
[19] W. Gao, A. Darvishan, M. Toghani, M. Mohammadi, O. Abedinia, and N. Ghadimi, “Different states of multi-block based forecast engine for price and load prediction,” International Journal of Electrical Power & Energy Systems, vol. 104, pp. 423-435, 2019.
[20] M. Gheydi, A. Nouri, and N. Ghadimi, “Planning in microgrids with conservation of voltage reduction,” IEEE Systems Journal, vol. 12, no. 3, pp. 2782-2790, 2016.
[21] 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, 2019.
[22] Y. Guo, X. Dai, K. Jermsittiparsert, and N. Razmjooy, “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, vol. 6, pp. 885-894, 2020.
[23] M. Ghiasi, N. Ghadimi, and E. Ahmadinia, “An analytical methodology for reliability assessment and failure analysis in distributed power system,” SN Applied Sciences, vol. 1, no. 1, p. 44, 2019.
[24] A. R. Gollou and N. Ghadimi, “A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets,” Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 4031-4045, 2017.
[25] M. T. Hagh, H. Ebrahimian, and N. Ghadimi, “Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG,” Frontiers in Energy, vol. 9, no. 1, pp. 75-90, 2015.
[26] M. Hamian, A. Darvishan, M. Hosseinzadeh, M. J. Lariche, N. Ghadimi, and A. Nouri, “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, vol. 72, pp. 203-212, 2018.
[27] E. Gholamian, P. Hanafizadeh, P. Ahmadi, and L. Mazzarella, “A transient optimization and techno-economic assessment of a building integrated combined cooling, heating and power system in Tehran,” Energy Conversion and Management, vol. 217, p. 112962, 2020.
[28] C. Lu, J. Wang, and R. Yan, “Multi-objective optimization of combined cooling, heating and power system considering the collaboration of thermal energy storage with load uncertainties,” Journal of Energy Storage, vol. 40, p. 102819, 2021.
[29] A. T. Aghaei and R. K. Saray, “Optimization of a combined cooling, heating, and power (CCHP) system with a gas turbine prime mover: A case study in the dairy industry,” Energy, vol. 229, p. 120788, 2021.
[30] W. Yanan, W. Jiekang, and M. Xiaoming, “Intelligent scheduling optimization of seasonal CCHP system using rolling horizon hybrid optimization algorithm and matrix model framework,” IEEE Access, vol. 6, pp. 75132-75142, 2018.
[31] Z.-X. Wang, Y.-F. Zhao, and L.-Y. He, “Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average,” Applied Soft Computing, p. 106475, 2020.
[32] L. Sun, X.-F. Han, Y.-P. Xu, and N. Razmjooy, “Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote Optimization Algorithm,” Energy Reports, vol. 7, pp. 7424-7433, 2021.
[33] Z. Yuan, W. Wang, H. Wang, and N. Razmjooy, “A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm,” Energy Reports, vol. 6, pp. 662-671, 2020.
[34] N. Razmjooy, M. Ashourian, and Z. Foroozandeh, “Metaheuristics and Optimization in Computer and Electrical Engineering,” ed: Springer.
[35] N. Razmjooy, V. V. Estrela, H. J. Loschi, and W. Fanfan, “A comprehensive survey of new meta-heuristic algorithms,” Recent Advances in Hybrid Metaheuristics for Data Clustering, Wiley Publishing, 2019.
[36] L. Yanda, 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, pp. 1-12, 2020.
[37] D. B. Mehdi Ramezani, Navid Razmjooy, “A New Improved Model of Marine Predator Algorithm for Optimization Problems,” Arabian Journal for Science and Engineering, 2021.
[38] W. Zhao, L. Wang, and Z. Zhang, “Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization,” IEEE Access, vol. 7, pp. 73182-73206, 2019.
[39] 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.
[40] G. Guo and N. Razmjooy, “A new interval differential equation for edge detection and determining breast cancer regions in mammography images,” Systems Science & Control Engineering, vol. 7, no. 1, pp. 346-356, 2019.
[41] R. Mousavifard, M. Abolghasemzadeh, N. Razmjooy, and Y. Alizadeh, “Optimal Design of Functionally Graded Steels Using Multi-objective Ant Lion Optimizer,” 2019.
[42] M. Yazdani and F. Jolai, “Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm,” Journal of computational design and engineering, vol. 3, no. 1, pp. 24-36, 2016.
[43] D. Amali and M. Dinakaran, “Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour,” Journal of Intelligent & Fuzzy Systems, no. Preprint, pp. 1-14, 2019.
[44] S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in engineering software, vol. 95, pp. 51-67, 2016.
[45] G. Zhang, C. Xiao, and N. Razmjooy, “Optimal parameter extraction of PEM fuel cells by meta-heuristics,” International Journal of Ambient Energy, pp. 1-10, 2020.
[46] Y. Cao, Q. Wang, Z. Wang, K. Jermsittiparsert, and M. Shafiee, “A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm,” Energy Reports, vol. 6, pp. 315-324, 2020.
[47] L. Zhao, Z. Li, H. Chen, J. Li, J. Xiao, and N. Yousefi, “A multi-criteria optimization for a CCHP with the fuel cell as primary mover using modified Harris Hawks optimization,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1-16, 2020.
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