期刊
APPLIED THERMAL ENGINEERING
卷 196, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2021.117233
关键词
District Heating; District Modelling; Model Predictive Control; Co-simulation; Modelica; Supervised Machine Learning
资金
- European Union's Horizon 2020 MOEEBIUS project [680517]
- H2020 Societal Challenges Programme [680517] Funding Source: H2020 Societal Challenges Programme
District heating systems in Europe play a crucial role in reducing building energy consumption, with potential for further optimization through model predictive control strategies. This paper presents a multiscale integrated district model concept that couples building and heating system models, showcasing its effectiveness in evaluating energy savings potential in existing systems.
District heating systems play a key role in reducing the aggregated heating and domestic hot water production energy consumption of European building stock. However, the operational strategies of these systems present further optimisation potential, as most of them are still operated according to reactive control strategies. To fully exploit the optimisation potential of these systems, their operations should instead be based on model predictive control strategies implemented through dedicated district energy management platforms. This paper describes a multiscale and multidomain integrated district model concept conceived to serve as the basis of an energy prediction engine for the district energy management platform developed in the framework of the MOEEBIUS project. The integrated district model is produced by taking advantage of co-simulation techniques to couple building (EnergyPlus) and district heating system (Modelica) physics-based models, while exploiting the potential provided by the functional mock-up interface standard. The district demand side is modelled through the combined use of physical building models and data-driven models developed through supervised machine learning techniques. Additionally, district production-side infrastructure modelling is simplified through a new Modelica library designed to allow a subsystem-based district model composition, reducing the time required for model development. The integrated district model and new Modelica library are successfully tested in the Stepa Stepanovic subnetwork of the city of Belgrade, demonstrating their capacity for evaluating the energy savings potential available in existing district heating systems, with a reduction of up to 21% of the aggregated subnetwork energy input and peak load reduction of 24.6%.
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