To set up a conda environment for the toy models, run in the terminal:
conda env create -f environment.yaml
To run a model, navigate to the directory scripts, then run in the terminal:
python run_model.py model_1
if you want to run model_1.
To plot the results, run this command in the terminal, and it should direct you to a web application where you can select different types of plots of the model you select.
calligraph your_model_results.nc
Or alternatively, substitute run_model.py in the second command with the plotting script that you want to call.
Assume we are modelling for city A that has gas power plants and a nuclear power plan with fixed total capacity. To meet the electricity demand of the city, the above-mentioned power plants and some renewable energy plants are in use.
Step 1. Run model 1 without any renewables capacity, then use calligraph to plot the results. Navigate to Timeseries plots, and choose 'flow*' in the drop-down list of Variable. Then, at the bottom of the page, choose 'Original resolution'.
- What do you observe as the dispatch pattern between gas and nuclear power plants?
- What is the electricity shadow price, and can you explain why it is at this value?
Step 2. Run model 1 and add 3 GW of onshore wind and 3 GW of solar in model_1/input/nodes.yaml. Plot the results again.
- What happens to the shadow price now? Why is it?
- What have you observed regarding the operation curves of wind and solar?
Assume we are modelling three countries: Germany, Switzerland and Italy. Each country has certain amount of available land for renewables with different capacity factors.
Step 1. Run model 2 as it sits now and plot the nodal shadow prices.
- Are they different? Why / why not?
Step 2. Change one parameter in the model or add one technology at one specific node to make the nodal shadow prices equivalent to each other.
- What did you do? Why would that work?
- Can you think of at least 3 ways to make it happen?
- Which one of them are the best in a real-world case, in your opinion, and why?