Model Factsheet

Overview / District Heating Transformation Network Topology Optimization (dhTransOPT)

Model District Heating Transformation Network Topology Optimization (dhTransOPT)

General Information
Name District Heating Transformation Network Topology Optimization
Acronym dhTransOPT
Methodical Focus Cost Optimization , Transformation/Decarbonization
Institution(s) University of Stuttgart
Author(s) (institution, working field, active time period) Prof. Dr.-Ing. Markus Blesl; active, Adrian Kaiserauer; M.Sc.; active, Frank Wendel; M.Sc.; inactive
Current contact person Prof. Dr.-Ing. Markus Blesl, Adrian Kaiserauer
Contact (e-mail) markus.blesl@ier.uni-stuttgart.de, adrian.kaiserauer@ier.uni-stuttgart.de
Website -
Logo
Primary Purpose Economic viability for district heating networks over full component lifetime and transformation of district heating technology and legislation
Primary Outputs Piping routes and diameters
Support / Community / Forum
Framework
Link to User Documentation -
Link to Developer/Code Documentation -
Documentation quality expandable
Source of funding Project-based
Number of developers less than 10
Number of users less than 10
Openness
Open Source
Planned to open up in the future
Costs None
Software
Modelling software Pyomo in Python
Internal data processing software Pandas
External optimizer Gurobi (optional)
Additional software (Q)GIS, Excel
GUI
Coverage
Modeled energy sectors (final energy) heat
Modeled demand sectors Households, Industry, Commercial sector
Modeled technologies: components for power generation or conversion
Renewables -
Conventional -
Modeled technologies: components for transfer, infrastructure or grid
Electricity -
Gas -
Heat distribution, transmission
Properties electrical grid -
Modeled technologies: components for storage -
User behaviour and demand side management
Changes in efficiency
Market models -
Geographical coverage
Geographic (spatial) resolution districts
Time resolution annual
Comment on geographic (spatial) resolution Theoretically adaptable, limited by computational power/time
Observation period 1 year, Transformation relevant periods, e.g., 25 years with milestone years
Additional dimensions (sector) -
Mathematical Properties
Model class (optimisation) MILP
Model class (simulation) -
Other
Short description of mathematical model class Linearized formulation of amended Kirchhoff laws
Mathematical objective costs
Approach to uncertainty Deterministic
Suited for many scenarios / monte-carlo
typical computation time less than an hour
Typical computation hardware Desktop
Technical data anchored in the model Yes, pipe and fluid properties in static input file
Model Integration
Interfaces GIS
Model file format .lp
Input data file format .txt, .xlsx
Output data file format .xlsx + plots (.svg/.html or similar)
Integration with other models
Integration of other models
References
Citation reference https://www.sciencedirect.com/science/article/pii/S0306261921017116
Citation DOI https://doi.org/10.1016/j.apenergy.2021.118494
Reference Studies/Models -
Example research questions Economic viability of an existing or planned DHN under shifting demand with decarbonization goals via temperature reduction and expansion
Model usage -
Model validation -
Example research questions Economic viability of an existing or planned DHN under shifting demand with decarbonization goals via temperature reduction and expansion
further properties
Model specific properties -

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DEU leitungsgebundene Wärme Decarbonisation Pathway investment costs specific investment Deutschland lifetime of investment district heating Wärme Nahwärme Renewable Zielsetzung Erneuerbare Erneuerbare Energien constraint Energiebedarf Szenario regional modelling renewable energy reducing demand Netzentwicklungsplan Fernwärme energy demand Heat Energiewende Germany temperature low temperature heat demand