  In this paper, we present a change point detection method for detecting
change points in multivariate nonstationary wind speed time series. The change
point method identifies changes in the covariance structure and decomposes the
nonstationary multivariate time series into stationary segments. We also
present parametric and nonparametric simulation techniques to simulate new wind
time series within each stationary segment. The proposed simulation methods
retain statistical properties of the original time series and therefore, can be
employed for simulation-based analysis of power systems planning and operations
problems. We demonstrate the capabilities of the change point detection method
through computational experiments conducted on wind speed time series at
five-minute resolution. We also conduct experiments on the economic dispatch
problem to illustrate the impact of nonstationarity in wind generation on
conventional generation and location marginal prices.
