Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building Clustering
Clean heating has not been widely applied in rural Chinese areas. Considering the abundance of solar energy resources, harvesting solar energy for heating can be an effective solution to the problem of space heating in most rural areas. As the disperse building distribution in rural areas makes it d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/3/1019 |
| _version_ | 1797488053530394624 |
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| author | Yanfeng Liu Deze Hu Xi Luo Ting Mu |
| author_facet | Yanfeng Liu Deze Hu Xi Luo Ting Mu |
| author_sort | Yanfeng Liu |
| collection | DOAJ |
| description | Clean heating has not been widely applied in rural Chinese areas. Considering the abundance of solar energy resources, harvesting solar energy for heating can be an effective solution to the problem of space heating in most rural areas. As the disperse building distribution in rural areas makes it difficult to implement centralized heating on a large scale, deploying centralized–decentralized hybrid solar heating system can achieve the best result from both the technical and economic perspectives. Taking a virtual village in Tibet as an example, this paper explores how to obtain optimal design of centralized–decentralized hybrid solar heating system based on building clustering. The results show that: (1) Compared with the fully centralized system and fully decentralized system, the centralized–decentralized hybrid solar heating system in the studied case could achieve a life cycle cost (LCC) saving of 4.8% and 2.3%, respectively; (2) The LCC of centralized–decentralized hybrid solar heating system basically decreases when the cost of the heating pipelines in the whole region decreases, but the emergence of single-household solar heating system may greatly increase the operating cost; (3) The necessity of designing a centralized–decentralized hybrid solar heating system can be determined by the pipeline price and building density, but the threshold values of pipeline price and building density are highly case-specific. |
| first_indexed | 2024-03-09T23:56:37Z |
| format | Article |
| id | doaj.art-9698b633e6b64b9195edb981d9968ff6 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T23:56:37Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9698b633e6b64b9195edb981d9968ff62023-11-23T16:23:46ZengMDPI AGEnergies1996-10732022-01-01153101910.3390/en15031019Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building ClusteringYanfeng Liu0Deze Hu1Xi Luo2Ting Mu3State Key Laboratory of Green Building in Western China, School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaState Key Laboratory of Green Building in Western China, School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaState Key Laboratory of Green Building in Western China, School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaState Key Laboratory of Green Building in Western China, School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaClean heating has not been widely applied in rural Chinese areas. Considering the abundance of solar energy resources, harvesting solar energy for heating can be an effective solution to the problem of space heating in most rural areas. As the disperse building distribution in rural areas makes it difficult to implement centralized heating on a large scale, deploying centralized–decentralized hybrid solar heating system can achieve the best result from both the technical and economic perspectives. Taking a virtual village in Tibet as an example, this paper explores how to obtain optimal design of centralized–decentralized hybrid solar heating system based on building clustering. The results show that: (1) Compared with the fully centralized system and fully decentralized system, the centralized–decentralized hybrid solar heating system in the studied case could achieve a life cycle cost (LCC) saving of 4.8% and 2.3%, respectively; (2) The LCC of centralized–decentralized hybrid solar heating system basically decreases when the cost of the heating pipelines in the whole region decreases, but the emergence of single-household solar heating system may greatly increase the operating cost; (3) The necessity of designing a centralized–decentralized hybrid solar heating system can be determined by the pipeline price and building density, but the threshold values of pipeline price and building density are highly case-specific.https://www.mdpi.com/1996-1073/15/3/1019density-based clusteringminimum spanning treesolar heating systemsystem optimizationgenetic algorithm |
| spellingShingle | Yanfeng Liu Deze Hu Xi Luo Ting Mu Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building Clustering Energies density-based clustering minimum spanning tree solar heating system system optimization genetic algorithm |
| title | Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building Clustering |
| title_full | Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building Clustering |
| title_fullStr | Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building Clustering |
| title_full_unstemmed | Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building Clustering |
| title_short | Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building Clustering |
| title_sort | design optimization of centralized decentralized hybrid solar heating system based on building clustering |
| topic | density-based clustering minimum spanning tree solar heating system system optimization genetic algorithm |
| url | https://www.mdpi.com/1996-1073/15/3/1019 |
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