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Introduction

pandapower is a powerful open-source library for static modeling of power systems.

Installation

1. Open a new terminal (with administrator rights, if on Windows). Create a new Python environment (called <env_name>) with Python < 3.10.
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conda create -n %ENV_NAME python=3.10

2. Activate the created environment.
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mamba activate %ENV_NAME

Then, install numba via mamba.

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mamba install numba==0.56.4

We require this particular version of numba to avoid a compatibility bug.

Numba module cannot be imported properly

I ran into the same issue. Downgrading numba to version 0.56.4 solves the problem at the moment. See the GitHub issue here: [bug] Incompatibility with numba 0.57.0 version information · Issue #2041 · e2nIEE/pandapower · GitHub



discourse.jupyter.org



The installation of numba through pip is discouraged since it depends on some C libraries that might not be compiled properly if installed otherwise. For more information, consult the numba documentation.

3. Create a new plain text file called requirements.txt. Open the file and paste the contents below in the file.
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Contents of requirements.txt
4. Install the requirements in the file requirements.txt by navigating with a terminal to the folder where the text file was created and executing the following command:
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pip install -r requirements.txt

5. Install seaborn and igraph for plotting, pytest for testing, tabulate for pretty table printing, and matpowercaseframes to read cases from MATPOWER.
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pip install seaborn igraph pytest tabulate matpowercaseframes

6. Install pandapower. The latest release version can be installed via pip by:
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pip install pandapower

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Installing Latest Development Version
7. (Optional) Install Jupyter Lab.
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pip install jupyterlab

If VSCode is the IDE of preference, install ipykernel

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pip install ipykernel

The previous installation is also possible via conda or mamba.

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mamba install ipykernel

8. Test your pandapower installation by running either of the following sample codes:
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Simple Power Flow Script
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Import Modules
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Running Tests

2024 © Sergio A. Dorado-Rojas