Tools and methods for modelling district heating systems a comprehensive comparison slideshare
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The document presents insights from the AEE Institute for Sustainable Technologies at the 4GDH Conference 2017, focusing on tools and methods for modeling district heating systems. It discusses various simulation tools (like Dymola, TRNSYS, and IDA-ICE), their advantages and limitations, and emphasizes the need for dynamic simulations to account for temperature wave propagation and zero-flow events. The findings highlight comparable results across simulation tools, but assert the importance of dynamic simulations for more complex scenarios, as well as the ongoing validation of models.
![AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13
Case 2: maximum / minimum pressure –
when?
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![AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13
Zero-flow events
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![AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13
Zero-flow events: supply pipe temperature
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![AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13
Zero-flow events: heat Loss in Consumer
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Heat losses in STANET: 3.11 kWh
Heat losses in Dymola: 7.04 kWh
Difference: 126% over 2.5 day period](https://image.slidesharecdn.com/toolsandmethodsformodellingdistrictheatingsystemsacomprehensivecomparisonslideshare-170913194315/85/Tools-and-methods-for-modelling-district-heating-systems-a-comprehensive-comparison-slideshare-23-320.jpg)
Tools and methods for modelling district heating systems a comprehensive comparison slideshare
- 1. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Astérix et le Chaudron, René Goscinny, Albert Uderzo, Darguad
- 2. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at AEE - Institute for Sustainable Technologies (AEE INTEC) 8200 Gleisdorf, Feldgasse 19, AUSTRIA AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at 2017/09/13 Tools and methods for modelling district heating systems: A comprehensive comparison Gerald Schweiger, Richard Heimrath, Peter Nageler, Keith O‘Donovan, Michael Salzmann, Harald Schrammel, Ingo Leusbrock
- 3. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Energy Supply and Energy System Planning Current fossil fuel based centrally organized mono-directional monosectoral decoupled stationary Future renewable (de-)centralized multi-directional intersectoral transient interactive intermodular
- 4. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 EnergySimCity: a modelling toolbox for urban energy systems Pre-Processing Co-Simulation Post-Processing Energy Supply Infrastructure Buildings Atmosphere Computatio- nal fluid dynamics Definition of bodies and climate Analysis temperature and flows Building simulation Building definition Analysis energy con- sumption Network simulation Network definition Analysis energy transport Simulation energy sources Definition energy sources Analysis energy supply Futureenergysystem
- 5. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 „We also can simulate DH systems with our tool!“
- 6. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 4th Generation District Heating
- 7. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Callenges from 4GDH for simulation & optimisation of DHC systems Features 4GDH Integrated part of smart energy systems Combining energy conservation with expansion of district heating Intelligent control and monitoring Loop layouts Renewable heat and waste heat low-temperature district heating for space heating and hot water Challenges for sim. / opt. tools Robustness & calculation speed Renewables / storage / prosumers Co-simulation Loops / rings / meshes Zero – Flow / Reverse Flow Mixed Integer / Unit Commitment Model predictive control Dynamic or static?
- 8. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Software tools Dymola (Modelica) •Equation-based modelling, object-oriented •Pro‘s: Reusability, extensibility, adaptability of models, well suited for optimization •Con‘s: difficult to go from a mathematical model to numerical solution algorithm TRNSYS •Causal modelling, dynamic •Pro‘s: Well established, block diagram modelling •Con‘s: not well suited for optimization, no parallelization possible Matlab Simulink •Causal modelling, dynamic •Pro‘s: robust, excellent for control application •Con‘s: not easy to read & code, reusability IDA-ICE •Equation based modelling, object-oriented •Pro‘s: Highly sophisticated models for buildings and HVAC, parallelizable, variable timestep simulation •Con‘s: No interface to FM I / Co-sim STANET •Stationary, domain-specific •Pro‘s: data import, GIS-interface, large networks, visualization of results •Con‘s: detailed sim of heat pumps, storages, substations, complex hydraulics/controls
- 9. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Workflow General comparison • Basics • Features • Comparison with literature results Case study • Pressure drops • Temperature wave propaga- tion • Heat losses • Maximum number of nodes Evaluation • Numerics, calculation speed • Ease of use, Co-Sim
- 10. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case study description 16 customers Evaluation of period 2016/02/01 – 2016/02/14 Load profiles based on building simulation 3 cases Case 1: Standard case, supply temperature 68 ˚C , return temperature at customer 43 ˚C, both fixed Case 2: Extension of different lines Case 3: Case 2 + temperature jump of 20K Supply line Longest distance
- 11. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case 2: maximum / minimum pressure 2.3 2.94 1.7 2.8 28.6 27.87 20.0 25.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 TRNSYS STANET DYMOLA IDA-ICE Pressure in bar Maximum pressure Minimum pressure
- 12. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case 2: maximum / minimum pressure – when? [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 TRNSYS STANET DYMOLA IDA-ICE Pressure in bar Maximum pressure Minimum pressure
- 13. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case 2: Heat supply, total heat losses and error energy balance 115 112 90 97 1198 1245 1169 1174 0 200 400 600 800 1,000 1,200 1,400 TRNSYS STANET DYMOLA IDA-ICE Energy in MWh Total heat supplied Heat losses Error in energy balance = 0% Error in energy balance = 7.7% Error in energy balance = 4.8% Error in energy balance = 0.4 %
- 14. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case 3: Temperature wave propagation temperature jump 40 50 60 70 80 90 100 2/1/2016 12:00 2/2/2016 0:00 2/2/2016 12:00 2/3/2016 0:00 Temperatureindeg.C. Time Inlet Inlet
- 15. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case 3: Temperature wave propagation: Dymola 40 50 60 70 80 90 100 2/1/2016 12:00 2/2/2016 0:00 2/2/2016 12:00 2/3/2016 0:00 Temperatureindeg.C. Time Inlet Dymola
- 16. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case 3: Temperature wave propagation: Dymola & TRNSYS 40 50 60 70 80 90 100 2/1/2016 12:00 2/2/2016 0:00 2/2/2016 12:00 2/3/2016 0:00 Temperatureindeg.C. Time Inlet TRNSYS Dymola Dymola: t(Tmax) = 03:45, Tmax = 76.7 deg.C TRNSYS: t(Tmax) = 07:15, Tmax = 70.1 deg.C
- 17. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Workflow 2.0 General compari- son • Basics • Features • Comparis on with literature results Validation • Single pipe measure- ments on lab scale • Small DH network KU Leuven Case study • Pressure drops • Tempera- ture wave propaga- tion • Heat losses • Maximum number of nodes Evaluation • Numerics, calcula- tion speed • Ease of use, Co- Sim
- 18. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case 1: Maximum number of nodes Simulation of one year
- 19. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Case 1: Maximum number of nodes Simulation of one year TRNSYS 10x possible Tedious process to generate larger networks STANET 100x possible Even larger networks possible IDA-ICE 80x possible, after that crash Dymola 20x possible, but calculation time 80 hours 10x: 160 customers, ~50 km net length 100x: 1600 customers, ~500km net length
- 20. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Do we need dynamic simulations? Temperature wave propagation static simulations capture general profile, but delay time can be significantly off depending on distance Static simulations do not capture pipe cooling patterns during longer periods of zero or close to zero flow events
- 21. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Zero-flow events 0.00 0.10 0.20 0.30 0.40 0.50 0.60 12/1/2014 0:00 12/1/2014 12:00 12/2/2014 0:00 12/2/2014 12:00 12/3/2014 0:00 Massflow[t/h] Dymola STANET
- 22. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Zero-flow events: supply pipe temperature 0 10 20 30 40 50 60 70 80 90 100 12/1/2014 0:00 12/1/2014 12:00 12/2/2014 0:00 12/2/2014 12:00 12/3/2014 0:00 Temperature[°C] Dymola STANET
- 23. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Zero-flow events: heat Loss in Consumer Supply Pipe 0 20 40 60 80 100 120 140 160 180 12/1/2014 0:00 12/1/2014 12:00 12/2/2014 0:00 12/2/2014 12:00 12/3/2014 0:00 HeatLoss[W] Dymola STANET Heat losses in STANET: 3.11 kWh Heat losses in Dymola: 7.04 kWh Difference: 126% over 2.5 day period
- 24. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Do we need dynamic simulations? Temperature wave propagation static simulations capture general profile, but delay time can be significantly off depending distance Static simulations do not capture pipe cooling patterns during longer periods of zero or close to zero flow events 4GDH components (storage, P2H, prosumers) experience high fluctuations in temperature which cannot be captured from static simulations Complex systems, control strategy But: Dynamic simulations are however significantly slower (for obvious reasons) and more complex Use static when possible, use dynamic when necessary?
- 25. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Conclusions and outlook Astérix et le Chaudron, René Goscinny, Albert Uderzo, Darguad
- 26. AEE – INSTITUTE FOR SUSTAINABLE TECHNOLOGIESwww.aee-intec.at Presentation AEE INTEC @ 4GDH conference 2017 | 2017/09/13 Conclusions and outlook All tools show comparable results for standard situations No need to develop new tools for DH modelling as Annex 60 pipe model / Carnot toolbox is open source More advanced situations need dynamic simulations Tradeoff: increased complexity, calculation speed Static simulations may under- and/or overestimate certain effects Validation to be finalized Conclusions then possible for error of indivual programs 2 papers in preparation Validation Case studies B.vanderHeijdeetal.,Dynamicequation-basedthermo-hydraulicpipemodel fordistrictheatingandcoolingsystems,EnergyConversionandManagement, https://doi.org/10.1016/j.enconman.2017.08.072
- 27. Thank you for your attention Ingo Leusbrock i.leusbrock@aee.at @leusbrocki
Editor's Notes
- #9: Modelica statt Dymola: Pro: equation based language: representing the physical structure of systems. It enables the modeller to model the system directly by the means of differential algebraic Reusability, extensibility, adaptability of models Simple to code and read code Well suited for optimization Large field of applications (chemical industry, automotive, airspace, heat, power, gas, buildings…) Interface to other tools (FMI) Cons: more difficult to go from a mathematical model to the numerical solution algorithm (because of the implicit formulation) .. wenn dir nicht ganz klar ist was da gemeint ist, tel ma noch einmal (das kann man an Beispielen schön deutlich Machen + kingt smart haha) Highly elabo- rate tools are required to handle them efficiently. Ida Ice (bin ich nicht Experte, würde aber meinen): Pros Highly sophisticated models for buildings and HVAC Equatoin based language (same pros as Modelica) Can read Modelica Cons: Specialized for builidngs – not really applications on a district sclae (I mean energy system, of course for buildings Not interface to FMI TRNSYS Well established in building simulation community (academa and industry) Some applications on district heating (Dresedn mit Trnsys TUD) Block diagramm modeling (advantages = cons of equatio based.. it is close to solution algo) Inteface to other tools (FMI) Cons: previous work has shown (see Wetter): not well suited for optimization Advantages of equations based (easy to read, code, reusability...) = disadvantage for block diagram modelling Based on Fortran language (= old, and not really high level ☺) Simulink Pros: Mit Abstand am weitesten verbreitet (mit Abstand) Robust and very good for control applications Interface to Matlab (= very powerful) Interface to other tools (FMI,) Cons Same as for TRNSYS… it is a block diagram modelling (= causal modeling language) STANET Commercial tool for dh, power system and water and therefore robust for this applications Cons Since it is not open it is not flexbile, not suited for robust prototyping, …
- #11: Differenzdruck bzw. Druckverläufe i. 0.5 bar am Schlechtpunkt Minimaldruck im System 2 bar Case 1: 4902 m Case 2: 20271 m
- #12: Slide11: da solltest du dazu sagen, dass der druck aufgrund des NT-ansatze ohne anpassung der Rohrleitungsdimensionen sehr hoch geworden ist – den die hohen drücke sind in der praxis recht unrealistisch..