Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review

Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen () and Shuangquan Liu ()
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Jie Zhang: System Operation Department, Yunnan Power Grid Co., Ltd., Kunming 650011, China
Xinchun Zhu: System Operation Department, Yunnan Power Grid Co., Ltd., Kunming 650011, China
Yigong Xie: System Operation Department, Yunnan Power Grid Co., Ltd., Kunming 650011, China
Guo Chen: School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Shuangquan Liu: System Operation Department, Yunnan Power Grid Co., Ltd., Kunming 650011, China

Energies, 2025, vol. 18, issue 13, 1-20

Abstract: In recent years, the increasing frequency of extreme weather events has led to a rise in unplanned unit outages, posing significant risks to the safe operation of power systems and underscoring the critical need for accurate prediction and effective mitigation of wind and solar power ramp events. Unlike traditional power forecasting, ramp event prediction must capture the abrupt output variations induced by short-term meteorological fluctuations. This review systematically examines recent advancements in the field, focusing on three principal areas: the definition and detection of ramp event characteristics, innovations in predictive model architectures, and strategies for precision optimization. Our analysis reveals that while detection algorithms for ramp events have matured and the overall predictive performance of power forecasting models has improved, existing approaches often struggle to capture localized ramp phenomena, resulting in persistent deviations. Moreover, current research highlights the necessity of developing evaluation systems tailored to the specific operational hazards of ramp events, rather than relying solely on conventional forecasting metrics. The integration of artificial intelligence has accelerated progress in both event prediction and error correction. However, significant challenges remain, particularly regarding the interpretability, generalizability, and real-time applicability of advanced models. Future research should prioritize the development of adaptive, ramp-specific evaluation frameworks, the fusion of physical and data-driven modeling techniques, and the deployment of multi-modal systems capable of leveraging heterogeneous data sources for robust, actionable ramp event forecasting.

Keywords: renewable energy; power prediction; ramp event; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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