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高比例风电系统的爬坡备用需求评估 被引量:10

Evaluation of Ramping Reserve Requirement for High-Proportion Wind Power Systems
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摘要 在极端天气情况下,风电功率会在短时间尺度内发生大幅度的变化,出现风电功率高风险爬坡事件,严重威胁电力系统的安全稳定运行。开展爬坡备用的需求评估,有助于减小风电出力波动和预测误差对电网运行带来的不利影响。为保障高比例风电系统的备用充裕度,提出一种基于门控循环单元和非参数核密度估计法的组合区间爬坡备用需求预测方法。首先,将风电功率实际数据和日前预测数据构建成多变量时间序列,基于门控循环单元(gate recurrent unit,GRU)模型提高预测结果的准确度。进而,采用非参数核密度估计方法对风电功率预测误差进行置信区间估计,得出给定置信区间下的风电功率预测区间。最后,根据区间预测结果,预测爬坡事件并提取爬坡特征量,建立爬坡备用需求评估模型,评估得出爬坡备用容量需求。基于西北某省级电网的数据开展了算例测试,验证了所提方法的有效性。 In extreme weather conditions,the power of wind power could change drastically within a short-time scale,giving rise to a high-risk ramp event,which seriously threatens the safe and stable operation of the power system. Carrying out the requirement assessment of the ramping reserve will help reduce the adverse effects of wind power output fluctuations and wind power forecast errors on the operation of the power grid. For this reason,this paper proposes a data-driven method for evaluating the requirement for ramping reserve. First,the actual wind power data and the day-ahead forecast data form a multivariate time series,and the accuracy of the forecast results is improved based on the gate recurrent unit(GRU) model.Furthermore, a non-parametric kernel density estimation method is used to estimate the confidence interval of the wind power forecast error,and the wind power forecast interval under a given confidence interval is obtained. Finally,according to the interval prediction results,the ramping event is predicted and the ramping feature is extracted, the ramping reserve requirement evaluation model is established,and the ramping reserve capacity requirement is estimated. An example test was carried out based on the data of a provincial power grid in Northwest China,which has verified the effectiveness of the method in this paper.
作者 王康 张青蕾 王泽 李骏 程程 文云峰 WANG Kang;ZHANG Qinglei;WANG Ze;LI Jun;CHENG Cheng;WEN Yunfeng(Electric Dispatch and Control Center,State Grid Shaanxi Electric Power Company Limited,Xi’an 710049,Shaanxi,China;School of Electrical and Information Engineering,Hunan University,Changsha 410082,Hunan,China)
出处 《电网与清洁能源》 北大核心 2022年第8期94-101,120,共9页 Power System and Clean Energy
基金 国家自然科学基金项目(52077066) 国网陕西省电力公司新能源消纳科技专项。
关键词 风电爬坡事件 区间预测 数据驱动 爬坡备用 wind power ramping event interval prediction data driven ramping reserve
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