Forecasting Scenario Generation for Multiple Wind Farms Considering Time-series Characteristics and Spatial-temporal Correlation

TLDR

Scenario forecasting methods have been widely studied to address wind power uncertainty, yet accurately capturing time‑series characteristics and spatial‑temporal correlations remains challenging. This study proposes a scenario generation method for multiple wind farms that integrates marginal distribution and dependence structure models. The method uses an ARIMA‑GARCH‑t model for marginal distributions and a time‑varying regular vine mixed copula (TRVMC) for spatial‑temporal dependence, generating scenarios from data of eight Northwest China wind farms and evaluating them on three aspects. The generated scenarios exhibit fluctuation, autocorrelation, and cross‑correlation patterns similar to actual wind power sequences.

Abstract

Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.