Design optimization and granularity analysis of district heating systems for distributed solar heating access

In the low-carbon transition process of district heating systems (DHS), the need for widespread access to renewable energy and lower carbon emissions conflict with the existing structure of large centralized DHS. This paper proposes an optimized design of the DHS based on granularity analysis methodology to harness distributed solar energy. This study applies a hierarchical clustering method to get the possible scheme set based on the given geometrical information of an area with distributed solar heating system (SHS). This study further carries out pipe network routings and the pipeline sizing optimization to implement the scheme with the best economic performance. To disclose the relationship between granularity and SHS access ratio, we propose a quantitative granularity estimation method considering the spatial distribution of heat in the sense of Manhattan distance. This study calculates optimal schemes' total annual cost (TAC) and potential operation instability (PRI) under a series of SHS access ratios. A real DHS covering a heating area of 164300 m2, and 21 buildings is selected for a case study. At a granularity of 4.50%, TAC is reduced by 16.04%, and PRI by 58.06% compared to the existing design where granularity is not considered and one substation covering the entire area. As the SHS access ratio increases from 0 to 12%, the optimal granularity value increases from 4.50% to 16.84%, indicating a higher granularity is necessary to utilize solar energy. Our work shows that a proper selection of granularity in the design stage is essential to the economic and stable operation of DHS with renewable energy access. The result from this study is also meaningful to the design and operation control of district energy systems and integrated energy systems.

Automated summary

This study proposes an optimized design of district heating systems (DHS) based on granularity analysis methodology to harness distributed solar energy. The study applies hierarchical clustering and pipeline sizing optimization to calculate the optimal scheme with the best economic performance. It also introduces a quantitative granularity estimation method. The research highlights the importance of proper selection of granularity in the design stage for the economic and stable operation of DHS.