JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Study on the Analysis of Correlation Decay Distance(CoDecDist) Model for Enhancing Spatial Prediction Outputs of Spatially Distributed Wind Farms
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Study on the Analysis of Correlation Decay Distance(CoDecDist) Model for Enhancing Spatial Prediction Outputs of Spatially Distributed Wind Farms
Hur, Jin;
  PDF(new window)
 Abstract
As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.
 Keywords
Spatial Correlation Analysis;Wind Generation Resources;Correlation Decay Distance Model;
 Language
Korean
 Cited by
 References
1.
K. Orwig and B. Karlson, "Wind energy 101", in 4th international conference on Integration of Renewable and Distributed Energy Resoruces, December 2010.

2.
A. Botterud, J. Wang, V. Miranda, and R. J. Bessa, "Wind power forecasting in U.S. electricity markets", The Electricity Journal, vol. 23, no. 3, pp. 71.82, April 2010.

3.
L. Xie, P. Carvalho, L. Ferreira, J. Liu, B. Krogh, N. Popli, andM. Ilic and, "Wind integration in power systems: Operational challenges and possible solutions", Proceedings of the IEEE, vol. 99, no. 1, pp. 214.232, Jan 2011. crossref(new window)

4.
M. Cellura, G. Cirrincione, A. Marvuglia, A. Miraoui, Wimd speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis, ScienceDirect, 1237-1250.

5.
N. Miller, "Wind and Solar Forecasitng", EPRI workshop, July, 2014.

6.
A. L. Rogers, J. W. Rogers, and J. F. Manwell, "Comparison of the performance of four measure-correlate-predict algorithms", Journal of Wind Engineering and Industrial Aerodynamics, vol. 93, no. 3, pp. 243-264, 2005. crossref(new window)

7.
Y. Gao and R. Billinton, "Adequacy assessment of generating systems containing wind power considering wind speed correlation", Renewable Power Generation, IET, vol. 3, no. 2, pp. 217-226, 2009. crossref(new window)

8.
R. Garka and I. Kruminiene, "Spatial analysis and prediction of curonian lagoon data with gstat", Mathematical Modelling and Analysis, vol. 9, no. 1, pp. 39.50, 2004.