I wanted to draw your attention to this story of an Californian community choice aggregator called Peninsula Energy, who are already at 99% of their energy matched on an hourly basis in 2023, and have published the open source model (AGPL) model they have used to plan their procurement to reach 100% 25/7 matched renewables by 2025.
Here’s the article and podcast. It’s pretty impressive:
Here’s the model on github they mentioned on github:
Here’s the white paper explaining more about how they thought they would do it:
Here’s the update whitepaper talking about how they’re doing now (they’re on track, basically)
A question I’d like to put this to the modelling nerds here - How applicable is this to other territories?
I didn’t see any of the PyPSA style models in the dependencies file, and I assumed that was the main tool used in Python land (at least I thought it was in use for @tom_brown 's recent work at PIK).
I know that California is blessed with all kinds of solar and wind resources compared to other places, but this is the first time I’ve seen any one talk about modelling 24/7 so aggressively, and actually executing so fast as well.
I think the MATCH model uses the SWITCH modelling framework developed by @mfripp and colleagues, one of many excellent Python-based frameworks. Not to be confused with LOADMATCH (there was a big lawsuit about that model).
The Princeton study on 24/7 by @JesseJenkins and colleagues looked at California and PJM, and TUB’s study on 24/7 looked at European countries.
Hope that helps!