2016
A Large Deviation based Splitting Estimation of Power Flow Reliability
Publication
Publication
Given the continued integration of intermittent renewable generators in electrical power grids, connection
overloads are of increasing concern for grid operators. The risk of an overload due to injection variability can
be described mathematically as a barrier crossing probability of a function of a multidimensional stochastic
process. Crude Monte Carlo is a well-known technique to estimate probabilities, but it may be computa-
tionally too intensive in this case as typical modern power grids rarely exhibit connection overloads. In this
paper we derive an approximate rate function for the overload probability using results from large devi-
ations theory. Based on this large deviations approximation, we apply a rare event simulation technique
called splitting to estimate overload probabilities more efficiently than Crude Monte Carlo simulation.
We show on example power grids with up to eleven stochastic power injections that for a fixed accuracy
Crude Monte Carlo would require tens to millions as many samples than the proposed splitting technique
required. We investigate the balance between accuracy and workload of three splitting schemes, each based
on a different approximation of the rate function. We justify the workload increase of large deviations based
splitting compared to naive splitting — that is, splitting based on merely the Euclidean distance to the rare
event set. For a fixed accuracy naive splitting requires over 60 times as much CPU time as large deviation
based splitting, illustrating its computational advantage. In these examples naive splitting — unlike large
deviations based splitting — requires even more CPU time than CMC simulation, illustrating its pitfall.
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doi.org/10.1145/2875342 | |
preprint (not CWI, to be used with submitted papers) | |
Organisation | Scientific Computing |
Wadman, W., Crommelin, D., & Zwart, B. (2016). A Large Deviation based Splitting Estimation of Power Flow Reliability. preprint (not CWI, to be used with submitted papers). doi:10.1145/2875342 |