Change point detection-based simulation of nonstationary sub-hourly wind time series

Sakitha Ariyarathne, Harsha Gangammanavar and Raanju R. Sundararajan

Applied Energy, 2022, vol. 310, issue C, No S0306261921017165

Abstract: In this paper, we present a wind speed simulation method by detecting change points in multivariate nonstationary wind speed time series data. The change point detection method identifies changes in the covariance structure and decomposes the nonstationary multivariate time series into stationary segments. Parametric and nonparametric techniques are also provided to model and simulate new time series within each stationary segment. The proposed simulation approach retains the statistical properties of the original time series (as obtained from a Numerical Weather Prediction system) 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.

Keywords: Change point detection; Stochastic programming; Nonstationary time series; Renewable energy (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.apenergy.2021.118501

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