The techno-economical parameters of the different technologies are the main drivers of the PyPSA modeling.
As such, they should be easy to validate by energy experts with e.g. experience in building energy projects.
I structured the data from the PNNL storage database so that it would be easy to see which technologies PyPSA may select. This gives the opportunity to limit the data validation effort to a subset of technologies.
The database structure makes it difficult to do this for all the data, considering the different technology classes (storage (electricity, heat, ...) , generation, transport/transmission, ... ). An approach would be to use more meta-data that allows easy visualization of essential parameters across all technologies.
The techno-economical parameters of the different technologies are the main drivers of the PyPSA modeling.
As such, they should be easy to validate by energy experts with e.g. experience in building energy projects. I structured the data from the PNNL storage database so that it would be easy to see which technologies PyPSA may select. This gives the opportunity to limit the data validation effort to a subset of technologies.
The database structure makes it difficult to do this for all the data, considering the different technology classes (storage (electricity, heat, ...) , generation, transport/transmission, ... ). An approach would be to use more meta-data that allows easy visualization of essential parameters across all technologies.