What is the current best practice in the ESM community (or, if it is complex, is there a rough approximation that is “good enough”)? ENTSO-E API has hydro filling, but only week by week. I would need hour by hour.
I would like to get more realistic grid electricity mixes, including uncertainty, for open life cycle assessment (https://github.com/BONSAMURAIS/bentso & https://bonsai.uno/).
This openmod topic on carbon intensity statistics might shed some light on your question. Grid mix and carbon intensity are closely related.
My long‑held view is that typical grid mixes and, when considering climate protection, the associated average carbon intensities are a poor substitute for integrated system modeling. Energy systems exhibit capacitated dynamics — more colloquially, lumpiness — whereby one additional kilowatt of demand can replace the system marginal generator and even reconfigure the entire dispatch stack. Questions of model fidelity and whether a particular approximation is “good enough” are therefore situation‑specific and have no generic answers. More advice on the topic of validation and verification can be found here. I guess life cycle assessment analysts prefer to use average metrics and dislike modeling the enclosing system out to its natural boundaries. Sometimes average metrics are legitimate, other times not. As classic merit order dispatch is being replaced by liberalized systems with high renewables shares, high resolution whole system modeling is becoming increasingly necessary. That, I would suggest, is current best practice.
Thanks @robbie.morrison for the helpful response (as usual)!
In my case, I am trying to improve the standard LCA way of modelling electricity grid mixes by including the uncertainty in generation shares. Currently, this is simply skipped and annual averages are used, because the LCA software is incapable of including uncertainty and then normalizing back to 1 kWh of production for the entire mix. We could use calculated covariance factors, but this is lossy and unnecessary when we have the raw historical market mixes.
You are completely correct that to get the right answer, one should model the entire system. It would be very nice to get some quantification on the error induced by using average factors e.g. for the charging electricity for storage systems, for the relationship between load and CO2 emissions, etc. Barring that, I guess using the average mix when the storage system is not discharging (maybe weighted by price, where lower prices have higher weights) could be a reasonable proxy for the charging mix. It would be lovely to get actual data from specific storage systems, but I have no idea where to find this for now.
If anyone is interested in helping build such a system, please come on over to https://github.com/BONSAMURAIS/bentso 
@cmutel Just putting on my admin hat. People should note that @‑handles are preferred over plain text names for a variety of reasons, both technical and social.