Adequacy Assessment of a Wind-Integrated System Using Neural Network-based Interval Predictions of Wind Power Generation and Load - CentraleSupélec Access content directly
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Adequacy Assessment of a Wind-Integrated System Using Neural Network-based Interval Predictions of Wind Power Generation and Load

Abstract

In this paper, we present a modeling and simulation framework for conducting the adequacy assessment of a wind-integrated power system accounting for the associated uncertainties. A multi-perceptron artificial neural network (NN) is trained by a non-dominated sorting genetic algorithm-II (NSGA-II) to forecast point-values and prediction intervals (PIs) of the wind power and load. The output of the assessment is given in terms of point-valued and interval-valued Expected Energy Not Supplied (EENS). We consider different scenarios of wind power and load levels, to explore the influence of the uncertainty in wind and load predictions on the estimation of system adequacy.
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Dates and versions

hal-00864843 , version 1 (23-09-2013)

Identifiers

  • HAL Id : hal-00864843 , version 1

Cite

Ronay Ak, Yan-Fu Li, Valeria Vitelli, Enrico Zio. Adequacy Assessment of a Wind-Integrated System Using Neural Network-based Interval Predictions of Wind Power Generation and Load. 2013. ⟨hal-00864843⟩
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