A robust probabilistic wind power forecasting method considering wind scenarios

Wind power forecasting is one of the cheapest and direct methods to alleviate negative impacts on power system reliability and stability from intermittent wind generation. Compared with deterministic forecasts, probabilistic forecasts can provide additional information concerning wind uncertainty for economic operation and efficient trading. However, it is far from ideal with respect to the accuracy, reliability and sharpness, since the wind shows strong variable property. In this paper, a robust probabilistic wind power forecasting method is proposed as (RPWPF) that can reflect the variability of wind generation under different wind conditions. The wind scenarios are identified concerning wind generation process and dominance of wind direction in a wind farm. And then, forecasting models for each scenario can be established and executed separately so that model parameters, such as kernel function and kernel width will be adjusted with the changing external wind conditions, wind speed and direction, in a real time operation. In this way, the forecasting model will provide more fined information on power outputs and their variabilities under different wind conditions. This proposed model is validated through comparison between the simulated power outputs and their variabilities under differing wind speeds and directions with the actual outputs on a practical 183 MW wind farm in northwest China. The results show that RPWPF achieves lower root mean square error comparing with artificial neural network model, while higher skill score for forecasting interval comparing with quantile regression. Finally, a sensitivity analysis is carried out to investigate the contribution of individual input (NWP variables) to help optimize the dimension of forecasting model.