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International Journal for Uncertainty Quantification
Factor de Impacto: 3.259 Factor de Impacto de 5 años: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Imprimir: 2152-5080
ISSN En Línea: 2152-5099

Acceso abierto

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2013003479
pages 185-204

UNCERTAINTY QUANTIFICATION IN DYNAMIC SIMULATIONS OF LARGE-SCALE POWER SYSTEM MODELS USING THE HIGH-ORDER PROBABILISTIC COLLOCATION METHOD ON SPARSE GRIDS

Guang Lin
Computational Science & Mathematics Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Department of Mathematics, School of Mechanical Engineering, Purdue University, West Lafayette, Indiana, USA
Marcelo Elizondo
Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, USA
Shuai Lu
Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, USA
Xiaoliang Wan
Department of Mathematics and Center of Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803

SINOPSIS

This paper employs a probabilistic collocation method (PCM) to quantify the uncertainties in dynamic simulations of power systems. The approach was tested on a single machine infinite bus system and the over 15,000 -bus Western Electricity Coordinating Council (WECC) system in western North America. Compared to the classic Monte Carlo (MC) method, the PCM applies the Smolyak algorithm to reduce the number of simulations that have to be performed. Therefore, the computational cost can be greatly reduced using PCM. A comparison was made with the MC method on a single machine as well as the WECC system. The simulation results show that by using PCM only a small number of sparse grid points need to be sampled even when dealing with systems with a relatively large number of uncertain parameters.


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