Adequacy assessment of a wind-integrated system using neural network-based interval predictions of wind power generation and load

被引：41

作者：

Ak, Ronay ^{[1
]}

Li, Yan-Fu ^{[2
]}

Vitelli, Valeria ^{[3
]}

Zio, Enrico ^{[1
,4
]}

机构：

[1] European Fdn New Energy Elect France, Chair Syst Sci & Energet Challenge, Cent Supelec, Paris, France

[2] Tsinghua Univ, Dept Ind Engn, Beijing, Peoples R China

[3] Univ Oslo, Oslo Ctr Biostat & Epidemiol, Dept Biostat, Domus Med, Oslo, Norway

[4] Politecn Milan, Dept Energy, Milan, Italy

来源：

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS | 2018年 / 95卷

关键词：

Adequacy assessment; Multi-objective genetic algorithm; Neural networks; Prediction intervals; Wind energy; SHORT-TERM LOAD; RELIABILITY EVALUATION; GENETIC ALGORITHMS; NSGA-II; DISPATCH; MODEL; UNCERTAINTIES; SIMULATION; REGRESSION; FORECASTS;

D O I：

10.1016/j.ijepes.2017.08.012

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a modeling and simulation framework is presented for conducting the adequacy assessment of a wind-integrated power system accounting for the associated uncertainties. A multi-layer perceptron artificial neural network (MLP NN) is trained by the non-dominated sorting genetic algorithm-II (NSGA-II) to forecast prediction intervals (PIs) of the wind power and load. The output of the adequacy assessment is given in terms of point-valued and interval-valued Expected Energy Not Supplied (EENS). Different scenarios of wind power and load levels are considered to explore the influence of uncertainty in wind and load predictions on the estimation of system adequacy. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：213 / 226

页数：14

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