NDLI: Estimation and characteristic analysis of aggregated generation of geographically distributed wind farms

Content Provider	IEEE Xplore Digital Library
Author	Jiang Wu Xiaohong Guan Xiaoxin Zhou Yuxun Zhou
Copyright Year	2011
Description	Author affiliation: China Electric Power Research Institute, Beijing, China (Xiaoxin Zhou) \|\| SKLMS Lab, MOE KLINNS Lab and the Systems Engineering Institute, Xian Jiaotong University, Shaanxi 710049, China (Jiang Wu; Xiaohong Guan; Yuxun Zhou)
Abstract	A good understanding of the aggregated stochastic characteristics of available wind generation is very important for secured and economic system operation in market environment. This paper focus on estimating and analyzing the aggregated generation of geographically distributed wind farms. A dynamic system model is formulated to describe the relationship between atmospheric and near-surface wind fields of the individual wind farms. A recursive algorithm based on Kalman filter theory is developed to update the parameters from NWP and to estimate the generation of individual wind farms based on their geographical locations, the wind dynamics in atmosphere, and the initial temporal and spatial conditions of the wind fields. The stochastic characteristics of the total available wind generation can then be estimated as the aggregation of the individual generation estimation. The actual data validates the assertion that the aggregated wind generation of distributed wind farms is less volatile than that of a single wind farm.
Starting Page	1
Ending Page	6
File Size	898901
Page Count	6
File Format	PDF
ISBN	9781457710001
ISSN	19449925
e-ISBN	9781457710025
e-ISBN	9781457710018
DOI	10.1109/PES.2011.6039810
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind farms Wind forecasting Wind speed Wind power generation Mathematical model Atmospheric measurements Numeric weather prediction Wind generation Kalman filter Dynamic system State estimation
Content Type	Text
Resource Type	Article

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