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| # - **LOLE** is the expected number of event-periods | ||
| # - **LOLD** is the expected number of event-days | ||
| # - **LOLEv** is the expected number of adequacy events | ||
| # |
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Are we adding LOLEv to this PR as well or just LOLD?
| # | LOLD | 1 | 10 | | ||
| # | ||
| # As we can see in the table above, even though LOLE and EUE are identical in this case, | ||
| # LOLD reveals that shortfall events are more dispersed in Case B. |
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Can we add a note that LOLD is only callable using Shortfallsamples?
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| # This creates a timestamped directory containing a `pras_results.json` | ||
| # file with: | ||
| # - system-level metrics (LOLE, EUE, NEUE) |
| end | ||
| end | ||
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| @testset "Multiple shortfall periods in same day count once" begin |
abdelrahman-ayad
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May 15, 2026
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abdelrahman-ayad
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Excellent work Akrivi! I left a couple of very minor comments. Can we also include metrics tests in the test/Results/metrics.jl ?
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This PR:
Loss of Load Days (LOLD)metric as defined in [1]Mathematical formulation
We define the Loss of Load Days (LOLD) metric as:
where:
and
We note that LOLD is not naturally defined at a single timestamp, since it aggregates over all time periods within a day. As a result, additional computation is needed compared to LOLE, namely:
Benchmarks
Complexity wise, the system wide and full-horizon LOLD scales with total simulation size, while the day-specifc queries scale only with the size of the selected day.
We also provide below a comparison of the benchmarks between LOLD and LOLE:
References:
[1] G. Stephen et al., "Clarifying the Interpretation and Use of the LOLE Resource Adequacy Metric," 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Manchester, United Kingdom, 2022, pp. 1-4, doi: 10.1109/PMAPS53380.2022.9810615.