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Abstract
Reliable district heating networks is of great importance for securing customers' heat demands. The current article aims to comprehensively analyze district heating networks reliability and buildings dynamics under failure condition. The functional reliability index is proposed as the ratio of the weighted expectations of buildings realistic heat gain to the desired heat demand. Network thermal-hydraulic analysis is done to determine buildings realistic heat gains, which is then placed weight revealing indoor temperature level. Building thermal time constant is introduced to analyze indoor thermal dynamics. Reliability assessment of a district heating network case in China is conducted to compare different schemes of transforming the original branched network to looped ones. The results show that functional reliability index increases from 0.736 to maximum of 0.829 increasing either pipeline capacity or backup pump capacity; correspondingly heat gain ratio of connected buildings increases from 32% to 100%, and indoor temperature when repair is done increases from 9.03°C to 18°C under the worst failure. Despite changing pipeline and backup pump capacity, the conventional reliability index stays constant. The developed functional reliability model can identify impacts of pipeline and backup pump capacity on system reliability and determine relative influence extent of them.
Funding
This work is supported by the National Natural Science Foundation of China (Grant No. 51508139) and the Fundamental Research Funds for the Central Universities (Grant No. HIT.NSRIF.2017058).
Additional information
Notes on contributors
Shanshan Cao
Shanshan Cao is a PhD Student. Peng Wang, PhD, is an Associate Professor. Wei Wang, PhD, is an Associate Professor. Yang Yao, PhD, is a Professor.
Peng Wang
Shanshan Cao is a PhD Student. Peng Wang, PhD, is an Associate Professor. Wei Wang, PhD, is an Associate Professor. Yang Yao, PhD, is a Professor.
Wei Wang
Shanshan Cao is a PhD Student. Peng Wang, PhD, is an Associate Professor. Wei Wang, PhD, is an Associate Professor. Yang Yao, PhD, is a Professor.
Yang Yao
Shanshan Cao is a PhD Student. Peng Wang, PhD, is an Associate Professor. Wei Wang, PhD, is an Associate Professor. Yang Yao, PhD, is a Professor.