Novel method to simulate large-scale thermal city models

Abstract This study presents a method used to simulate large-scale thermal models of cities that achieves two improvements compared to the state-of-the-art techniques: 1) Current state-of-the-art methods cannot simulate the dynamic interaction between subcomponents of a smart energy system at urban scale. This method proposes detailed dynamic simulation approaches for large-scale thermal models. 2) Currently applied co-simulation frameworks are not applicable to large-scale models. In the present study, the dynamic building simulation tool IDA Indoor Climate and Energy, which uses parallelization methods for large-scale models, is coupled with a co-simulation platform. The methods are applied to a semi-virtual case study, which consists of 1561 buildings and a new development area. The building stock is analyzed using an automated method based on publicly available data. In contrast, the virtual urban development area is investigated using a co-simulation framework with three dynamic simulation tools: IDA Indoor Climate and Energy for buildings (256 thermal zones and 29 heating systems), TRNSYS for the energy supply unit and Dymola/Modelica for the district heating network. The influence of co-simulation on the accuracy and on the computation time are investigated. The major finding of this study is that the computation time can be significantly reduced by decoupling methods.

[1]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[2]  Michael Wetter,et al.  Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed , 2011 .

[3]  Mark Jennings,et al.  A review of urban energy system models: Approaches, challenges and opportunities , 2012 .

[4]  Helge V. Larsen,et al.  Equivalent models of district heating systems: for on-line minimization of operational costs of the complete district heating system , 1999 .

[5]  Christoph Hochenauer,et al.  Generation Tool for Automated Thermal City Modelling , 2019 .

[6]  H. Madsen,et al.  Modelling the heat consumption in district heating systems using a grey-box approach , 2006 .

[7]  Joseph Andrew Clarke,et al.  Energy Simulation in Building Design , 1985 .

[8]  Gerald Schweiger,et al.  The potential of power-to-heat in Swedish district heating systems , 2017 .

[9]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[10]  Frédéric Kuznik,et al.  Modeling the heating and cooling energy demand of urban buildings at city scale , 2018 .

[11]  Florian Judex,et al.  Implementation of an automated building model generation tool , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).

[12]  Ercan Atam,et al.  Current software barriers to advanced model-based control design for energy-efficient buildings , 2017 .

[13]  Jochen Dahm,et al.  District Heating Pipelines in the Ground - Simulation Model - , 2001 .

[14]  Dirk Müller,et al.  Dynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems , 2017 .

[15]  Kevin Sartor,et al.  Experimental validation of heat transport modelling in district heating networks. , 2017 .

[16]  Ernst Andreas Koch,et al.  Continuous Simulation for Urban Energy Planning Based on a Non-Linear Data-Driven Modelling Approach , 2016 .

[17]  Dejan Mumovic,et al.  A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .

[18]  Eui-Jong Kim,et al.  Urban energy simulation: Simplification and reduction of building envelope models , 2014 .

[19]  Dirk Müller,et al.  Iea Ebc Annex 60 Modelica Library – An International Collaboration to Develop A Free Open-Source Model Library for Buildings And Community Energy Systems , 2015, Building Simulation Conference Proceedings.

[20]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[21]  Rita Streblow,et al.  Workflow automation for combined modeling of buildings and district energy systems , 2016 .

[22]  Christoph F. Reinhart,et al.  Shoeboxer: An algorithm for abstracted rapid multi-zone urban building energy model generation and simulation , 2017 .

[23]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[24]  Engin Yesil,et al.  Tracking Time Adjustment In Back Calculation Anti-Windup Scheme , 2006 .

[25]  Per Sahlin,et al.  IDA Simulation Environment a tool for Modelica based end-user application deployment , 2003 .

[26]  Gerald Schweiger,et al.  District heating and cooling systems – Framework for Modelica-based simulation and dynamic optimization , 2017 .

[27]  Niklaus Kohler,et al.  Building age as an indicator for energy consumption , 2015 .

[28]  Christoph Hochenauer,et al.  Novel validated method for GIS based automated dynamic urban building energy simulations , 2017 .

[29]  Brian Vad Mathiesen,et al.  4th Generation District Heating (4GDH) Integrating smart thermal grids into future sustainable energy systems , 2014 .

[30]  Tobias Loga,et al.  TABULA building typologies in 20 European countries—Making energy-related features of residential building stocks comparable , 2016 .

[31]  Christoph F. Reinhart,et al.  Autozoner: an algorithm for automatic thermal zoning of buildings with unknown interior space definitions , 2016 .

[32]  J. Kämpf,et al.  A simplified thermal model to support analysis of urban resource flows , 2007 .

[33]  Arno Schlueter,et al.  Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts , 2015 .