Adequacy assessment of a wind-integrated system using neural network-based interval predictions of wind power generation and load. (February 2018)

• Record Type: Journal Article
• Authors: Ak, Ronay; Li, Yan-Fu; Vitelli, Valeria; Zio, Enrico
• Is Part Of: International journal of electrical power & energy systems. Volume 95(2018)
• Page Start: 213
• Page End: 226
• Publication Date: 2018-02
• Subjects: Adequacy assessment -- Multi-objective genetic algorithm -- Neural networks -- Prediction intervals -- Wind energy; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2017.08.012
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A framework for adequacy assessment of a wind-integrated power system is presented. Load fluctuations, volatility of wind speed, and component failures are considered. Two approaches are proposed for point-valued and interval-valued EENS estimation. The effectiveness of the proposed approaches is confirmed compared with MC simulation. Abstract: 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.