Change Point Detection in Nonstationary Sub-Hourly Wind Time Series

TL;DR

This paper introduces a change point detection method for multivariate nonstationary wind speed time series, enabling segmentation into stationary periods and facilitating realistic simulation for power system analysis.

Contribution

It proposes a novel change point detection technique focusing on covariance changes and develops simulation methods that preserve statistical properties of wind data.

Findings

Effective detection of change points in wind data

Simulation methods that retain original statistical features

Impact analysis of nonstationarity on power system economics

Abstract

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.