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Resources 2015, 4(1), 70-92; doi:10.3390/resources4010070

A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales

1
Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO 80401, USA
2
Department of Applied Mathematics, University of Colorado at Boulder, Boulder, CO 80309, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Simon J. Watson
Received: 17 December 2014 / Accepted: 27 January 2015 / Published: 12 February 2015
(This article belongs to the Special Issue Spatial and Temporal Variation of the Wind Resource)
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Abstract

Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating only wind speed, or power, or only the wind vector at a particular location and sampling frequency. In this work, we introduce a Markov-switching vector autoregressive (MSVAR) model, and we demonstrate its flexibility in simulating wind vectors for 10-min, hourly and daily time series and for individual, locally-averaged and regionally-averaged time series. In addition, we demonstrate how the model can be used to simulate wind vectors at multiple locations simultaneously for an hourly time step. The parameter estimation and simulation algorithm are presented along with a validation of the important statistical properties of each simulation scenario. We find the MSVAR to be very flexible in characterizing a wide range of properties in the wind vector, and we conclude with a discussion of extensions of this model and modeling choices that may be investigated for further improvements. View Full-Text
Keywords: bivariate time series; Markov-switching; vector autoregressive; wind direction; wind speed bivariate time series; Markov-switching; vector autoregressive; wind direction; wind speed
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hering, A.S.; Kazor, K.; Kleiber, W. A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales. Resources 2015, 4, 70-92.

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