Rare-event splitting simulation for analysis of power system blackouts

Wang, Sing-Po; Chen, Argon; Liu, Chih-Wen; Chen, Chun‐Hung; Shortle, John

doi:10.1109/pes.2011.6039280

Cited by 16 publications

(12 citation statements)

References 15 publications

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“…The model is similar to, and inspired by, a number of models in the literature -for example, Bae and Thorp [2], Carreras et al [4], Chen et al [7], Newman et al [20], Wang et al [26]. While the model is relatively simple, the spirit is to capture global blackout dynamics through representative behavior of key elements.…”

Section: Introductionmentioning

confidence: 99%

Efficient simulation of blackout probabilities using splitting

Shortle

2013

International Journal of Electrical Power & Energy Systems

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Section: Introductionmentioning

confidence: 99%

Efficient simulation of blackout probabilities using splitting

Shortle

2013

International Journal of Electrical Power & Energy Systems

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“…Splitting has been applied to cascading power failures in several papers such as Wang et al [9], Kim et al [12], and Shortle et al [20]. For an excellent introduction and overview of the application of splitting to power systems, see [12].…”

Section: Splitting Simulation For Power Gridmentioning

confidence: 99%

“…Shortle [19] also considered factor 3). Wang et al [9] applied the results of [16] to cascading blackout simulation. Kim et al [12] independently applied splitting to power-systems simulation, focusing on estimating the probability distribution of the number of line outages.…”

Section: Splitting Simulation For Power Gridmentioning

confidence: 99%

Efficient Splitting Simulation for Blackout Analysis

Wang

Chen

Liu

et al. 2015

IEEE Trans. Power Syst.

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The analysis of severe blackouts has become an essential part of transmission grid planning and operation. This may include evaluation of rare-event probabilities, which can be difficult to estimate. While simulation offers flexibility to model large complex systems, efficiency remains a big concern when estimating very small probabilities. This paper presents an effective simulation technique to evaluate rare-event probabilities associated with cascading blackouts in an electric grid. We test our technique on an IEEE 118-bus electric network and show that it can dramatically improve simulation efficiency. We also demonstrate that the proposed technique can effectively locate vulnerable links. These are links whose failures lead to the highest probabilities of a blackout event.

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“…Also splitting techniques have been applied to power systems. [Wang et al, 2011] and [Shortle, 2013] estimated small probabilities of instantaneous, cascading failures of grid components. Instead, in our work the rare event is a connection overloading during a certain time interval and the sources of uncertainty are the power injections.…”

Section: Introductionmentioning

confidence: 99%

A Large-Deviation-Based Splitting Estimation of Power Flow Reliability

Wadman

Crommelin

Zwart

2016

ACM Trans. Model. Comput. Simul.

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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 wellknown technique to estimate probabilities, but it may be computationally 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 deviations 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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Rare-event splitting simulation for analysis of power system blackouts

Cited by 16 publications

References 15 publications

Efficient simulation of blackout probabilities using splitting

Efficient simulation of blackout probabilities using splitting

Efficient Splitting Simulation for Blackout Analysis

A Large-Deviation-Based Splitting Estimation of Power Flow Reliability

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