Design Optimization of Centralized–Decentralized Hybrid Solar Heating System Based on Building Clustering

Abstract

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.

Keywords

• density-based clustering
• minimum spanning tree
• solar heating system
• system optimization
• genetic algorithm