Modelling of District Heating Systems

Comparative Evaluation of White-box Modelling Approaches

Abstract

District heating systems are prevalent in most European countries, and such energy delivery methods can be crucial to decarbonisation objectives. To appropriately size and design the control of such networks, the modelling of district heating networks should have a good representation of the demand-side, which is the set of buildings connected to the network. In-stead of simplified modelling of the demand, whole-building simulation tools can be invoked in this case, like EnergyPlus. More recently, equation-based libraries have been developed in Mod-elica for component-based simulation of HVAC systems. Modelica-based libraries offer easier model composability and are particularly interesting for control fine-tuning; on the downside, the model setup can be more complex, with more validation needed. This paper conducts a comparative study of the Modelica LBNL Buildings library against Ener-gyPlus, based on an archetype-based hypothetical case in the UK with a small-scale district heating system. The methodology resides on models developed in the two tools with the same level of modelling detail. The comparison helps understand software differences in the model-ling procedure, computational time, relative accuracy of energy predictions and heating system variables. The results indicate Modelica Buildings library yields similar accuracy in terms of heat transfer calculation through thermal zones as EnergyPlus, whilst capturing additional en-ergy consumption caused by the dynamic changes at system startup and the realistic controllers used in the Modelica district heating models. Meanwhile, the Modelica Buildings library’s out-puts show the fluctuations of system variables, indicating different operation patterns and con-trol effects against EnergyPlus. This study also proves that the Modelica Buildings library is the better tool for district heating simulation in the context of dynamic performance evaluation and control testing, based on overall capabilities, limitations, and prediction differences.