ARIMA-Based Time Series Model of Stochastic Wind Power Generation

• Published in: IEEE transactions on power systems Vol. 25; no. 2; pp. 667 - 676
• Main Authors: Peiyuan Chen, Pedersen, Troels, Bak-Jensen, Birgitte, Zhe Chen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ARIMA processes; Autoregressive processes; Correlation; Driving; Markov models; Markov processes; Offshore; Power measurement; Power system modeling; Power system planning; Probability distribution; Stochastic processes; Stochasticity; Temporal logic; time series; Wind energy; Wind farms; Wind power; Wind power generation; Wind speed
• DOI: 10.1109/TPWRS.2009.2033277
• ISSN: 0885-8950

This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA) process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation and probability distribution. The LARIMA model outperforms a first-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power generation.