Solar Power Ramp Event Forewarning with Limited Historical Observations

Abstract

Timely and accurate forewarning of solar power ramp events (SPREs) is crucial for a power system operation. In this article, a novel forewarning method for SPREs is proposed based on credal network (CN) and imprecise Dirichlet model (IDM). A new expression of SPRE is proposed, which focuses on the power change caused by meteorological fluctuation. Considering that the single-valued probability may not provide convincing results in case of insufficient ramp event records, probability interval is adopted to reflect the ambiguous correlation between SPREs and meteorological conditions. The meteorological evidences are mapped to ramp events by a CN to enhance the sensitivity of SPRE identification. The structure of the CN is established by the learning algorithm with respect to the relations between SPRE and meteorological conditions, as well as different meteorological conditions. Furthermore, an extended IDM is developed to estimate the interval-valued parameters in the CN. Then, a credal classifier is established to output the ramp forewarning result. The effectiveness of the proposed method is verified through case studies, and obvious improvement on the accuracy of ramp forewarning can be seen.