DHNx Addons

This package contains a collection of functions useful for workflows with DHNx, LPagg, and GIS-data in general in the context of municipal heat planning.

Some functions of the script are specific to Germany.

This is not a stable release and breaking changes will occur often and without warning.

Example workflow

This package provides a default workflow that perfoms the following:

• Take a polygon defining an area as input
• Download OpenStreetMap building and street data
• Assign a status "heated" depending on the type of each building
• Assign a random distribution of construction years
• Assign a random refurbishment status depending on the building type and construction year based on typical distributions from the literature
• Assign a specific heat demand based on construction year, refurbishment status and building type from the literature
• Estimate domestic hot water demand based on the building type
• Calculate the heated reference area based on the building ground area from the OpenStreetMap-Data and an estimation of the number of floors
• Apply climate correction factor based on the TRY-region
• Based on the gathered heat demand for each building, create load profiles for each building with LPagg
• As weather data, the old DWD TRY (2011) is used for the appropriate region
• (It is recommended to download and use the current DWD TRY (2017) data for your location from https://kunden.dwd.de/obt/)
• Choose a random building as a producer for a district heating grid
• Optimize the installation of a district heating grid along the streets with DHNx, choosing paths and required diameters for the pipes
• Simulate the heating grid to determine pressure loss, flow rate and temperature distribution within the network

Installation

TLDR

This project needs to be installed with pip, because not all dependencies are found on conda.

Create an environment (named work in this example) with either venv

python -m venv work
source work/bin/activate  # on Linux
work\Scripts\activate  # on Windows

or conda

conda create --name=work python=3.13
conda activate work

then install dhnx_addons with its dependencies via pip:

pip install "dhnx_addons @ https://github.com/jnettels/dhnx_addons/archive/main.tar.gz"

(This installs the package from this GitHub repository. dhnx_addons is not yet published on pypi.)

Detailed Information

• Create a dedicated python virtual environment or conda environment for the project
• If you want to use conda, the recommended installation is miniconda from https://www.anaconda.com/download/success
  • On windows, if the Terminal is used with PowerShell, do not forget to run conda init powershell (which might require administrator rights)
• For development work:
  • Install git, e.g. with winget install Git.Git if available
  • Download (clone) this repository with git clone https://github.com/jnettels/dhnx_addons.git
  • Change directory into the new folder cd dhnx_addons
  • Installed the package in editable mode with pip install -e .[dev]
• If you want to use your environment in Spyder, you will likely need to install spyder-kernels. But Spyder will inform about the required version if necessary
• dhnx requires a solver to perform its optimization, e.g. the free cbc or gurobi (which is faster)
• The solver cbc (https://github.com/coin-or/Cbc/releases/latest) is installed automatically to ~\coin-or-cbc by the example workflow, if no solver is detected. Its location is added to the system path only during runtime, so it might not be available in other scripts
• If the user is eligible, an academic license for gurobi can be obtained at https://www.gurobi.com/downloads/end-user-license-agreement-academic/
• To test the example OpenStreetMap workflow, run python examples/dhnx_example.py