Add CVAR and NCVAR resource adequacy reporting - #201
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| + ( | ||
| (pd.Timedelta('5D') if sw.GSw_HourlyType == 'wek' else pd.Timedelta('1D')) | ||
| - pd.Timedelta('1h') | ||
| - pd.Timedelta('1H') |
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The Pacific run test on the github actions failed with this error (see log output in the "run ReEDS model" step here):
solve.py | 2026-08-27 22:52:34 | ERROR | ValueError: Invalid frequency: 1H. Failed to parse with error message: ValueError("Invalid frequency: H. Failed to parse with error message: KeyError('H'). Did you mean h?")
Given that I think we should revert these to '1h' to '1H' changes. Was this change working for your runs? If it was, were you testing with a clean reeds environment?
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yes I had the same issue when testing, fixed and pushed the change.
| for i, row in eue_events.iterrows(): | ||
| dates.append( | ||
| pd.Series(index=pd.date_range(row.start, row.end, freq='h'), data=1) | ||
| pd.Series(index=pd.date_range(row.start, row.end, freq='H'), data=1) |
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Looks like another one to revert
| pd.Series(index=pd.date_range(row.start, row.end, freq='H'), data=1) | |
| pd.Series(index=pd.date_range(row.start, row.end, freq='h'), data=1) |
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| ### Check all stress criteria; for regions that fail, add new stress periods |
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Let's keep this comment
| ### Check all stress criteria; for regions that fail, add new stress periods | |
| ### Check all stress criteria; for regions that fail, add new stress periods |
| write_surplus=False, | ||
| write_energy=False, | ||
| write_shortfall_samples=False, | ||
| write_shortfall_samples_totals=True, |
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It sounds like PRAS.ShortfallSamples() increases memory use within PRAS, but NatLabRockies/PRAS#109 fixes it. Can we check the PRAS memory use on a full-US run on this branch and on main before merging (I think Kodi has done this before)? If it goes up a lot, we should probably wait to merge this PR until we can switch to a new PRAS release that includes this PRAS PR.
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This PR continues the work and review discussion from #160. The updated implementation was moved to an upstream ReEDS branch so the GitHub Actions tests can run.
Most of the review comments from #160 were addressed in
bs/cvar; this branch is based on that work and includes the remaining dependency-related changes.Branch is tested against the corresponding main baseline using USA_defaults. The two runs produced identical core model results: capacity, generation, new capacity, retirements, curtailment, transmission, electricity prices, and present value of system cost all matched in the final year, with no observed differences in the comparison plots. The additional CVAR/NCVAR reporting therefore does not appear to affect the ReEDS solution.
Runtime increased slightly, as expected, due to writing sample-level PRAS shortfall totals and calculating the additional RA metrics.
Illustrative CVAR/NCVAR diagnostics
The figures below illustrate the new CVAR/NCVAR reporting using the
USA_defaultstest case. The run used 100 PRAS samples andGSw_PRM_CVARalpha = 0.95, so each CVAR estimate is the mean of the five largest sample-total shortfalls.PRAS sample-total shortfalls span the full 15-weather-year resource adequacy horizon and are annualized before being reported. These results are intended to validate the calculation. The national results show substantial variation in tail shortfall across solve years. The regional diagnostics indicate that the national tail risk is often concentrated in a small number of regions, with ND_MISO accounting for the largest regional CVAR in several of the illustrated years.