GMM-HMM-Based Medium- and Long-Term Multi-Wind Farm Correlated Power Output Time Series Generation Method

Abstract

Medium- and long-term wind power output time series are required in stochastic programming model for power system planning. Hidden Markov model (HMM) is a common method to generate wind power output time series, which can simultaneously consider the temporal and spatial correlation of multiple wind farms. However, the existing HMM methods use discrete matrix or Gaussian distribution to describe the output distribution of multiple wind farms, which usually leads to a relatively large error in statistical indices between the generated time series and the historical time series. Therefore, this paper proposes a method for generating medium- and long-term correlated output time series of multiple wind farms based on the Gaussians mixture model-Hidden Markov model (GMM-HMM). The discrete state variable in the hidden Markov model is used to describe the meteorological state. The Markov chain between discrete state variables is used to describe the temporal correlation of wind power output. The wind power output vector of multiple wind farms is used as the observation variable, and the mixed Gaussian probability distribution mapping relationship between the state variable and the multidimensional wind power output vector is established. Based on the Monte Carlo sampling method, the multi-wind farm output series satisfying the spatiotemporal correlation of historical output series are generated monthly. In the calculation example, the monthly wind power output series generated by five wind farms in Jilin Province are analyzed. The results show that the main statistical characteristics of the multi-wind power output time series generated by the proposed method are generally superior to those obtained with the traditional wind power output modeling method, which proves the superiority of the proposed method.