Downscaling deconstruction, hybrid semi-mechanism state estimation and cascaded dynamic equivalent modelling of complex district heating networks

District heating system is important for modern urban integrated energy system. To reduce carbon emissions, its intelligent operation via cyber-physical fusion is concerned, where dynamic modelling with low complexity, high precision, and fast computability becomes critical. To solve this problem, a systematic approach including downscaling deconstruction, hybrid semi-mechanism (HSM) state estimation and cascaded dynamic equivalent modelling of complex district heating network (DHN) is offered. Firstly, to describe a DHN under an organized mathematical framework, it is expressed as a direct graph whose adjacency matrix is extracted for node clustering via customized Tarjan algorithm. It yields several segmented clusters, seen as the downscaling deconstruction results. Secondly, for cases where physical sensors are missing or damaged, leading to data missing for cascade modelling, a HSM state estimation method is proposed to provide reliable information support. Thirdly, through integrated modelling for cascaded dynamics of segmented clusters, complex thermodynamic properties of a DHN can be achieved. Finally, a DHN in northern China is adopted for validation using measured data. The results indicate that the state estimation method proposed in this paper can effectively achieve high-precision integrated modelling of a DHN, huge potential for optimal operation to improve operational efficiency and reduce operating costs.