Since the PR: PyPSA/PyPSA#669, my feeling is that this code block is not necessary. I am happy to get assigned to this task and deal with it (as well as the Linopy workshop)

Some sector-coupling technologies have multiple ouputs (e.g. CHP plants producing heat and power). By default, PyPSA links have only one input (bus0) and one output (bus1) with a given efficieny (efficiency). Thus, we have to tell PyPSA that links can have multiple outputs by overriding the component attributes.

override_component_attrs = pypsa.descriptors.Dict(
    {k: v.copy() for k, v in pypsa.components.component_attrs.items()}
)

override_component_attrs["Link"].loc["bus2"] = [
    "string",
    np.nan,
    np.nan,
    "2nd bus",
    "Input (optional)",
]
override_component_attrs["Link"].loc["efficiency2"] = [
    "static or series",
    "per unit",
    1.0,
    "2nd bus efficiency",
    "Input (optional)",
]
override_component_attrs["Link"].loc["p2"] = [
    "series",
    "MW",
    0.0,
    "2nd bus output",
    "Output",
]

Hi Fabian,

would it be possible to share the solutions for the Excercise Time Series Analysis? I'm currently struggling a bit with the task to fill up the NaN prices with data the week ahead.

Best regards,
Johannes

environment.yaml is the file name

Hi, this command doesn't seem to work:
mamba create -f environment.yml

there is an issue with this, documented here: mamba-org/mamba#633
There are a couple of workarounds proposed on this issue page, including one that worked for me - create an empty env with mamba, then update it based on --file environment.yaml

Awesome notebooks! Is there interest to convert the pyomo notebook to a linopy version?
Happy to work on this if you think it's useful

Add to your intro what this website is about & what people will learn. Basically:
"This semester I taught a new course at TU Berlin on energy system modelling and data science, for which I built a small website with energy-focused Python tutorials. The course offers many hands-on introductions to various libraries that are useful for energy system modelling and processing data more generally. It includes tutorials and examples for getting started with Python, numpy, matplotlib, pandas, geopandas, cartopy, rasterio, atlite, networkx, pyomo, pypsa, plotly, hvplot, and streamlit. Topics covered include:

• time series analysis (e.g. wind and solar production)
• tabular data (e.g. LNG terminals)
• geographical data (e.g. location of power plants)
• data visualisation
• converting weather data to renewable generation
• land eligibility analysis (e.g. where to build wind turbines)
• optimisation
• electricity market modelling
• power flow modelling (linearised)
• capacity expansion planning
• sector-coupling"
• interactive visualisation and dashboarding"

Would it make sense to add a small and concise introduction to git and github?
This introduction aims to teach the git concept and the difference between a soft & hard fork. The learning would be especially important for people aiming to join the development e.g. PhDs/ postdocs/professionals/ others. We would refine and extend the course from here to reach the standard of your other offered courses.

Happy to get allocated to this if there is interest.