| Units: | - |
| Default Value: | 0 |
| Validation Rule: | In (0,1,2,3) |
| Description: | Stochastic optimization method for PASA. |
PASA Stochastic Method sets the algorithm used in PASA to resolve multi-sample data (defined by Variable objects), where the number of samples is set by the Stochastic Risk Sample Count associated with the executing Model. This is relevant when there exist Variables that change properties that affect PASA such as Region Load or capacity. The attribute can take the following values:
- Deterministic (value = 0)
- The expected value is used for sample data. For variables using endogenous sampling this means that the Profile value is used, and for variables that read their sample values from multi-band input, the first band is used (the assumption is that the first band is the expected value).
- Sequential Monte Carlo (value = 1)
- PASA runs S times, one time for each sample, choosing the appropriate values for each. This yields S complete Maintenance Factor results, which are then fed into the calculation of the required number of Outage Pattern Count. These independent samples are executed in sequence.
- Parallel Monte Carlo (value = 3)
- As above but the independent samples are executed in parallel i.e. all samples are executed at the same time on separate threads.
- Stochastic (value = 2)
- PASA runs a single optimization incorporating all S samples into a stochastic optimization, and yielding a single Maintenance Factor profile that is optimal with respect to the uncertainty in the data. The required Outage Pattern Count all use this profile when generating their maintenance events.
The simulation phases that run after PASA (MT Schedule and ST Schedule) use maintenance patterns generated based on the Maintenance Factor calculated by PASA. If either of the multi-sample options is chosen here then the samples in those simulation phases will use maintenance patterns for the corresponding sample in PASA. This improves the outcome of each sample in ST Schedule.
See also: