Journal
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
Volume 9, Issue 4, Pages 837-848Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.35833/MPCE.2020.000935
Keywords
Scenario generation; wind farm; regular vine Copula; spatial-temporal correlation; time-series characteristics
Categories
Funding
- National Key Research and Development Program of China [2017YFB0902600]
Ask authors/readers for more resources
This paper proposes a method to generate scenarios for multiple wind farms using the ARIMA-GARCH-t model and TRVMC model, and evaluates the effectiveness on a dataset of 8 wind farms in Northwest China. The results show that the generated scenarios are similar to the actual wind power sequences in terms of fluctuation characteristics, autocorrelation, and crosscorrelation.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available