Minimization of wind farm operational cost based on data-driven models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

18 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number6479369
Pages (from-to)756-764
Journal / PublicationIEEE Transactions on Sustainable Energy
Volume4
Issue number3
Online published13 Mar 2013
Publication statusPublished - Jul 2013

Abstract

Scheduling a wind farm in the presence of uncertain wind speed conditions is presented. Two scheduling models, the base model and the stochastic optimization model, are developed by integrating mathematical programming and data mining. A migrated particle swarm optimization algorithm is developed for solving the two scheduling models. The solution computed by this algorithm determines the operational status and control settings of a wind turbine. The cost of operating a wind farm according to the solutions of both scheduling models closely matches the cost computed based on a schedule under a perfect information scenario. The computational results provide insights into the management and operation of wind farms. © 2010-2012 IEEE.

Research Area(s)

  • Data mining, migrated particle swarm optimization, mixed-integer programming, scheduling, stochastic optimization, wind farm