摘要
风电不可避免的随机性、间歇性和不确定性给并网、电力调度与消纳带来巨大挑战。通过风电功率预测对风电波动进行有效评估,对于降低风电不确定性风险推进风电稳步发展具有重要意义。针对当前大规模风电发展中至关重要的短期风功率预测精度问题,介绍了风电短期预测误差来源及影响,分类阐述了确定性和不确定性风电功率短期预测方法原理、优缺点、评价指标及适用性,从异常数据的检测与清洗、缺失数据的重构、数据特征的选择或提取、数据聚类、数据分解、优化算法改进和考虑物理模型等方面,探讨并综述了风电功率预测精度提升关键技术及其最新研究进展,最后对未来风电功率预测技术发展趋势进行了展望,为提升风电功率短期预测精度、推进精细化预测技术发展、保障系统安全稳定运行提供参考。
The inevitable randomness,intermittence and uncertainty of wind power bring great challenges to grid connection,power dispatching and consumption.Effective assessment of wind power fluctuations through wind power forecasting is of great significance to reduce the risk of wind power uncertainty and promote the steady development of wind power.Aiming at the crucial problem of short-term wind power prediction accuracy in the current development of large-scale wind power,the sources and influences of short-term wind power prediction error are introduced,the principles,advantages and disadvantages,accuracy evaluation criteria and applicability of deterministic and probabilistic short-term wind power forecasting methods are elaborated.The latest research progress of key technologies to improve the accuracy of wind power prediction from the aspects of abnormal data detection and cleaning,missing data reconstruction,data feature selection or extraction,data clustering,data decomposition,optimization algorithm improvement and consideration of physical model are investigated and summarized,and finally the prospect of future development trend of wind power prediction technology is concluded,providing a reference for improving the short-term prediction accuracy of wind power,promoting the development of refined prediction technology,and ensuring the safe and stable operation of the system.
作者
唐新姿
顾能伟
黄轩晴
彭锐涛
TANG Xinzi;GU Nengwei;HUANG Xuanqing;PENG Ruitao(School of Mechanical Engineering,Xiangtan University,Xiangtan 411105;Mingyang Smart Energy Group Co.,Ltd.,Zhongshan 528437)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2022年第12期213-236,共24页
Journal of Mechanical Engineering
基金
国家自然科学基金(51305377,51975504)
湖南省教育厅(19B539)
湖南省自然科学基金(2021JJ30676)资助项目。
关键词
风电功率预测
预测精度
深度学习
区间预测
组合预测模型
wind power forecasting
prediction accuracy
deep learning
interval prediction
combined forecasting model
