The role of spatial correlations in Monte Carlo studies on power system

Seguinot, Baptiste; Zani, Alessandro

doi:10.1109/ptc.2015.7232392

Cited by 1 publication

(1 citation statement)

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“…Therefore, the spatial correlations cannot be fully represented. Reference [12] pointed out that the reliability of a system will be significantly overestimated if spatial correlations are neglected. In [13], a spatial-temporal load model was proposed for the electric power distribution facilities.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Spatial-Temporal Features of Bus Loads in Electric Grids Through Clustering in a Dynamic Model Space

Zhang¹,

Mu²,

Song

et al. 2020

IEEE Access

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Bus loads in electric grids have inherently a spatial-temporal behavior and also a certain degree of randomness. The spatial-temporal feature based bus load forecasting, which provides additional information on the spatial distribution and the uncertainty of future electric loads, is of importance to power systems dispatching and planning, in particular, with intermittent renewable power generation. In this paper, a method for extracting spatial-temporal features, including abnormal states of multiple bus loads in electric grids, is proposed. The abnormal spatial load states are firstly identified by using one-class support vector machine. Then, only the load fluctuations of normal states are mapped into a dynamic model space supported by polynomials in order to approximate the time series of bus loads. The parameters of polynomials are clustered by the Dirichlet process mixture model for deriving the patterns of load state evolution. As a result, the extracted spatial-temporal patterns are a set of different distributions of bus loads with static features and dynamic features displayed explicitly. The method is tested against the bus loads of an electric grid in a city in the Northeast China. The proposed methodology is validated with respect to the bus loads in time slots of the future 10 days.INDEX TERMS Load forecasting, multiple bus loads, one-class support vector machine, spatial-temporal feature.

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Section: Introductionmentioning

confidence: 99%