Code and analysis used for calculating the merit order effect of renewables on price and carbon intensity of electricity markets
Project description
Merit-Order-Effect
Code and analysis used for calculating the merit order effect of renewables on price and carbon intensity of electricity markets
Repo Publishing - To Do
Notebook Polishing Changes:
- Add docstrings (can be one-liners unless shown in the user-guides or likely to be used often)
- Add a mini sentence or two at the top of each nb explaining what it's about
- Ensure there is a short explanation above each code block
- Move input data to a raw dir
- Check all module imports are included in settings.ini
- Re-run all of the notebooks at the end to check that everything works sequentially
Completed Notebooks:
- Retrieval
- EDA
- LOWESS (start with the biggy)
- Price Surface Estimation
- Price MOE
- Carbon Surface Estimation and MOE
- Prediction and Confidence Intervals
- Hyper-Parameter Tuning
- Tables and Figures
New Code:
- Separate the binder and development
environment.yml
files - Re-attempt LIGO fitting example as part of a user-guide
- Add in the prediction and confidence interval plots
- Add a lot more to the EDA examples
- Every week re-run a single analysis (could be in the user-guide) and show the generated fit at the top of the ReadMe
- Try to speed things up, e.g. with Numba (one person has already started doing this)
- Get the models saved on S3 or figshare and pulled into binder via a postBuild script
External/ReadMe
- Add the GH action for version assignment triggering pypi push and zenodo update
- Just before the paper is published set the version to 1.0.0 and have a specific Binder link that builds from that version as stored in the Zenodo archive
- Could link the zotero collection
- Add citations for both the external data I use and the resulting time-series I generate
- Add bibtex citation examples for both the paper and the code (could use this)
- Publish the latest version to PyPi
- Mention the new module in the gist that some of the basic regression code was inspired by
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