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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722009465 |
| _version_ | 1811178067053248512 |
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| author | Zhiguo Wang Haoyu Chen Xiao Sun Haibing Lu Tianyi Wang |
| author_facet | Zhiguo Wang Haoyu Chen Xiao Sun Haibing Lu Tianyi Wang |
| author_sort | Zhiguo Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T06:13:12Z |
| format | Article |
| id | doaj.art-9e8b38870a9740e98cfedd4c216b8579 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-11T06:13:12Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-9e8b38870a9740e98cfedd4c216b85792022-12-22T04:41:09ZengElsevierEnergy Reports2352-48472022-10-018379393Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithmZhiguo Wang0Haoyu Chen1Xiao Sun2Haibing Lu3Tianyi Wang4School of Mechanical Engineering, Xi’an Shiyou University, Xi’an 710065, China; Corresponding authors.School of Mechanical Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaShaanxi Province Key Laboratory of CO2 Sequestration and Enhanced Oil Recovery, Xi’an 710065, ChinaResearch Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China; Corresponding authors.Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, ChinaIn 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.http://www.sciencedirect.com/science/article/pii/S2352484722009465Solar energyAir source heat pumpParticle swarm algorithmOptimizationSensitivity |
| spellingShingle | Zhiguo Wang Haoyu Chen Xiao Sun Haibing Lu Tianyi Wang Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm Energy Reports Solar energy Air source heat pump Particle swarm algorithm Optimization Sensitivity |
| title | Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm |
| title_full | Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm |
| title_fullStr | Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm |
| title_full_unstemmed | Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm |
| title_short | Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm |
| title_sort | optimizing the solar air hybrid source heat pump heating system based on the particle swarm algorithm |
| topic | Solar energy Air source heat pump Particle swarm algorithm Optimization Sensitivity |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722009465 |
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