Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm
In order to deal with the increasingly severe energy situation and climate change, reducing global carbon emissions and developing new energy have become a universal consensus among countries in the world. The design of clean energy heating systems such as solar collectors (SC) and air source heat p...
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| Veröffentlicht in: | Energy reports Jg. 8; S. 379 - 393 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.10.2022
Elsevier |
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| ISSN: | 2352-4847, 2352-4847 |
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| Abstract | In order to deal with the increasingly severe energy situation and climate change, reducing global carbon emissions and developing new energy have become a universal consensus among countries in the world. The design of clean energy heating systems such as solar collectors (SC) and air source heat pumps (ASHP) has also received widespread attention. However, optimizing multiple parameters that interact with each other in the hybrid heating systems such as solar-air hybrid source heat pumps (HSHP) is still challenging, and the optimization of the parameters remains to be studied. By using the TRNSYS simulation platform, modify the performance parameters to decrease the system’s annual cost with particle swarm optimization (PSO) and coordinate search method (CSM), respectively. The results show that two algorithms can significantly enhance the system performance, where it is the easier for PSO to find global optimum, and the average performance index COPsys of the system is about 15% higher than that of the CSM, and the system’s annual power consumption could be lowered by 27.75%; In addition, the matching principle of the key parameters of the hybrid heating system is proposed and the sensitivity ranking of the optimized parameters is derived. These results offer theoretical foundations for optimal design of the solar-air HSHP heating system. |
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| AbstractList | In order to deal with the increasingly severe energy situation and climate change, reducing global carbon emissions and developing new energy have become a universal consensus among countries in the world. The design of clean energy heating systems such as solar collectors (SC) and air source heat pumps (ASHP) has also received widespread attention. However, optimizing multiple parameters that interact with each other in the hybrid heating systems such as solar-air hybrid source heat pumps (HSHP) is still challenging, and the optimization of the parameters remains to be studied. By using the TRNSYS simulation platform, modify the performance parameters to decrease the system’s annual cost with particle swarm optimization (PSO) and coordinate search method (CSM), respectively. The results show that two algorithms can significantly enhance the system performance, where it is the easier for PSO to find global optimum, and the average performance index COPsysof the system is about 15% higher than that of the CSM, and the system’s annual power consumption could be lowered by 27.75%; In addition, the matching principle of the key parameters of the hybrid heating system is proposed and the sensitivity ranking of the optimized parameters is derived. These results offer theoretical foundations for optimal design of the solar-air HSHP heating system. In order to deal with the increasingly severe energy situation and climate change, reducing global carbon emissions and developing new energy have become a universal consensus among countries in the world. The design of clean energy heating systems such as solar collectors (SC) and air source heat pumps (ASHP) has also received widespread attention. However, optimizing multiple parameters that interact with each other in the hybrid heating systems such as solar-air hybrid source heat pumps (HSHP) is still challenging, and the optimization of the parameters remains to be studied. By using the TRNSYS simulation platform, modify the performance parameters to decrease the system’s annual cost with particle swarm optimization (PSO) and coordinate search method (CSM), respectively. The results show that two algorithms can significantly enhance the system performance, where it is the easier for PSO to find global optimum, and the average performance index COPsys of the system is about 15% higher than that of the CSM, and the system’s annual power consumption could be lowered by 27.75%; In addition, the matching principle of the key parameters of the hybrid heating system is proposed and the sensitivity ranking of the optimized parameters is derived. These results offer theoretical foundations for optimal design of the solar-air HSHP heating system. |
| Author | Lu, Haibing Chen, Haoyu Wang, Tianyi Wang, Zhiguo Sun, Xiao |
| Author_xml | – sequence: 1 givenname: Zhiguo surname: Wang fullname: Wang, Zhiguo email: zhgwang@xsyu.edu.cn organization: School of Mechanical Engineering, Xi’an Shiyou University, Xi’an 710065, China – sequence: 2 givenname: Haoyu surname: Chen fullname: Chen, Haoyu organization: School of Mechanical Engineering, Xi’an Shiyou University, Xi’an 710065, China – sequence: 3 givenname: Xiao surname: Sun fullname: Sun, Xiao organization: Shaanxi Province Key Laboratory of CO2 Sequestration and Enhanced Oil Recovery, Xi’an 710065, China – sequence: 4 givenname: Haibing surname: Lu fullname: Lu, Haibing email: luhaibing69@petrochina.com.cn organization: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China – sequence: 5 givenname: Tianyi surname: Wang fullname: Wang, Tianyi organization: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China |
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| Cites_doi | 10.1002/er.5057 10.1016/j.enbuild.2014.11.030 10.1016/j.enbuild.2011.04.028 10.1016/j.egypro.2012.11.074 10.17485/ijst/2015/v8i32/87749 10.1016/j.rser.2013.05.032 10.1145/321062.321069 10.1016/j.proeng.2013.02.063 10.1016/j.enbuild.2013.11.065 10.1016/j.energy.2011.05.026 10.1016/S1359-4311(00)00105-8 10.1016/j.ijrefrig.2020.03.031 10.1016/j.rser.2020.110253 10.1016/j.enbuild.2011.03.033 10.1038/nature11475 10.14419/ijet.v7i4.13280 10.1177/0143624412462592 |
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| Keywords | Air source heat pump Sensitivity Particle swarm algorithm Optimization Solar energy |
| Language | English |
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| Title | Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm |
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