Estimating missing hourly profiles for hydro

[grgmiller]

When assigning an hourly profile to monthly hydroelectric data, the current method is to use the cleaned hydro profiles from EIA-930 if available, and assign a flat hourly profile to each month otherwise. In exploring the data, there are a couple of ways that this methodology should be improved.

Pumped strorage hydro

Currently conventional hydroelectric and pumped storage hydroelectric (PSH) are grouped together, both in our cleaned EIA-923 values and in the EIA-930 data. It appears that at least in some cases, certain BAs are reporting net negative hydro generation in certain hours, which would reasonably represent PSH charging. Things to investigate:

• consider separating conventional hydroelectric from PSH in the EIA-923 data, since one is primarily a storage technology. This would be relatively easy to do because conventional hydroelectric is identified with the HY prime mover code, and PSH is identified with the PS prime mover code.
• Currently, I believe that negative generation values reported in EIA-930 are being treated as anomolous in our data cleaning process and being filtered out of the data. We may not want to filter these out for hydroelectric, but this perhaps raises a much deeper conversation about how to treat energy storage data more broadly.

Avoiding use of flat profiles for hydroelectric

Although hydroelectic often displays a significant amount of seasonal variation, many hydro generators (especially reservoirs/dams) exhibit significant variation in generation across hours of a day. We might want to consider how we could estimate this hourly variation if we do not have direct data for the hydro facilities operating in that BA. Several options:

• consider using hydro data from directly interconnected balancing authorities. The reasoning here is that regionally, hydro may be operated in similar patterns, especially if it represents a large portion of electricity interchange.
• Perhaps investigate a month-hour fixed effects model for hydro generation based on national data from EIA-930
• Consider whether data for reservoir hydro and run of river hydro can be separated, since these likely have different dispatch patterns. However, there does not seem to be any directly-reported data that could be used to directly identify hydro facilities in this way (although it is possible that there is another database that contains this information).
• It is possible that daily variation in hydro dispatch is load following, and thus could be modeled using regional demand data.
• Another method that was used in this paper was to use stream guage data downstream of each hydroelectric facility to estimate production. However, this sounds like a labor-intensive process and may not be super accurate (eg how would you determine flow through the turbine vs spill-over).

[grgmiller]

This paper mentions a dataset that might be helpful: https://bescdn.breakthroughenergy.org/publications/US_Test_System_with_High_Spatial_and_Temporal_Resolution.pdf