Probability Forecasting of Wind Power Ramp Events Using a Time Series Similarity Search Algorithm

With the increase in penetration of wind generation on interconnected power systems, the importance of wind power ramp forecasting has continuously grown. Large power ramps caused by sudden weather changes raise more concerns due to their significantly impact on the power system economics and stability. Correct wind power ramp forecasts can help the utility companies to trade off the risks when scheduling wind energy in the electricity market. In this paper, a novel probability forecasting algorithm has been developed for wind power ramp prediction based on a similarity search technology with an empirical probability estimation. Compared with traditional ramp event forecasting methods which identify the ramps from the scenario generation forecasts, the proposed method can effectively reduce the impact of the uncertainty from both the generation forecast model and the transition process by establishing a direct connection between the wind speed forecasts and the wind power ramp prediction.