内蒙古某风电场爬坡事件特征分析及阈值确定

doi:doi:10.19912/j.0254-0096.tynxb.2024-0846

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

内蒙古某风电场爬坡事件特征分析及阈值确定

更新时间：2025-10-22

- 内蒙古某风电场爬坡事件特征分析及阈值确定
- Vol. 46, Issue 9, Pages: 133-142(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0846
  CLC： P49
- Published Online：14 October 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
[J]. 2025, 46(9): 133-142.
DOI：

[J]. 2025, 46(9): 133-142. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0846.

摘要

以内蒙古某风电场为研究区

利用2019年9月11日19:00—2020年12月31日23:45逐15 min风电机组轮毂高度风速及实际发电功率数据

对上、下爬坡事件进行识别

从爬坡高度、爬坡速率、持续时间以及季节、日变化差异等方面对爬坡事件特征展开分析

并结合电力系统运行风险评估

将爬坡事件划分为极低、低、中、高、极高5类风险等级

进一步确定不同风险爬坡事件可能出现的最大爬坡高度及风速变化。结果表明：上爬坡事件的平均爬坡速率明显高于下爬坡事件

爬坡速率与爬坡时间呈明显反比关系;爬坡事件的爬坡高度具有明显季节变化

春夏两季的平均爬坡高度与爬坡速率均高于秋冬两季;发生高风险上爬坡事件的可能性明显大于高风险下爬坡事件

存在发生极高风险下爬坡事件的可能

不同风险下上爬坡事件与下爬坡事件的最大爬坡高度相当

但下爬坡事件最大风速变化更大。

Abstract

The fluctuation and intermittency of wind power influenced by meteorological environmental conditions are important reasons leading to wind power ramp. A wind farm in Inner Mongolia is taken as the study area. The data of wind speeds on the height of wind turbine hub and actual power data are used to identify the ramp events in the study area. The data time interval is 15 minutes and period is from 19:00 on September 11

2019 to 23:45 on December 31

2020. The characteristics of the ramp events are analyzed from the aspects of ramp heights

ramp speeds

times of duration

seasonal and daily variations

and so on. Further more

five risk levels of the ramp events including extremely low

low

medium

high and extremely high are classified

combining with the risk assessment of power system operation. The maximum ramp heights and wind speed changes at different risk levels are determined in order to provide effective technical support for predicting ramp events. The results show that： 1）The average ramp speed of the upward ramp events is significantly higher than that of the downward ramp events

the ramp speeds are inversely proportional to duration times; 2） the ramp heights and ramp speeds have significant seasonal variations

the average of those in spring and summer are higher than in autumn and winter; 3） the occurrence possibility of high risk upward ramp events is significantly higher than that of high risk downward ramp events

there is a possibility of extremely high risk downward ramp events

the maximum ramp height of upward ramp events is close to that of downward ramp events under different risk levelss

but the maximum wind speed variation of downward events is greater.

关键词

Keywords

references

陶玉波, 陈昊, 秦晓辉, 等. 短期风电功率预测概念和模型与方法[J]. 电力工程技术, 2018, 37(5): 7-13.

张东英, 代悦, 张旭, 等. 风电爬坡事件研究综述及展望[J]. 电网技术, 2018, 42(6): 1783-1792.

ELA E, KIRBY B.ERCOT event on February 26,2008:lessons learned[R].USA:National Renewable Energy Laboratory, 2008.

熊一, 査晓明, 秦亮, 等. 基于强对流天气判别的风功率爬坡预报方法研究[J]. 中国电机工程学报, 2016, 36(10): 2690-2698.

KUSIAK A, ZHENG H Y, SONG Z.Wind farm power prediction: a data-mining approach[J]. Wind energy, 2009, 12(3): 275-293.

李芬, 李春阳, 闫全全, 等. 多时间尺度下光伏出力爬坡事件概率建模与评估研究[J]. 太阳能学报, 2019, 40(11): 3289-3298.

朱文立, 张利, 杨明, 等. 考虑日周期性影响的光伏功率爬坡事件非精确概率预测[J]. 电力系统自动化, 2019, 43(20): 31-38.

匡洪海, 王建辉, 张瀚超, 等. 一种新型的风电功率爬坡段识别方法[J]. 电网技术, 2019, 43(5): 1752-1759.

KHODAYAR M E, SHAHIDEHPOUR M.Stochastic price-based coordination of intrahour wind energy and storage in a generation company[J]. IEEE transactions on sustainable energy, 2013, 4(3): 554-562.

刘申. 基于气象测量场的风电爬坡事件与功率预测[D]. 济南: 山东大学, 2015.

FERREIRA C, GAMA J, MATIAS L, et al.A survey on wind power ramp forecasting[R].USA:Argonne National Laboratory (ANL),2011.

郭燕玲, 赵晶, 周林, 等. 山东半岛风电爬坡事件的识别与天气分析研究[J]. 气候与环境研究, 2017, 22(1): 97-107.

OHBA M, KADOKURA S, NOHARA D.Impacts of synoptic circulation patterns on wind power ramp events in East Japan[J]. Renewable energy, 2016, 96: 591-602.

余洋, 陈东阳, 王卜潇, 等. 基于IBSO-SDT的风电爬坡事件检测方法[J]. 太阳能学报, 2023, 44(9): 348-355.

杨茂, 刘红柳. 超短期风电功率爬坡事件对风电功率实时预测误差的影响研究[J]. 太阳能学报, 2017, 38(3): 571-577.

黄棋悦, 严楠, 钟旭佳. 基于生成对抗网络的风电爬坡功率预测[J]. 太阳能学报, 2023, 44(1): 226-231.

任双雪, 王毅钊, 张寓涵, 等. 风电功率爬坡事件的滑动窗检测与实例分析[J]. 电网与清洁能源, 2018, 34(1): 109-114.

崔明建. 风电功率爬坡事件问题研究[D]. 武汉: 武汉大学, 2015.

沈小军, 付雪姣, 周冲成, 等. 风电机组风速-功率异常运行数据特征及清洗方法[J]. 电工技术学报, 2018, 33(14): 3353-3361.

宗阳. 风电功率爬坡事件检测及预测方法研究[D]. 南京: 南京信息工程大学, 2020.

张颖超, 宗阳, 邓华, 等. 基于趋势特征的风电功率爬坡事件检测方法[J]. 电测与仪表, 2020, 57(18): 122-127, 132.

涂娇娇. 风电功率波动特性分析及其在电力系统中的应用[D]. 吉林: 东北电力大学, 2015.

邢丽珠, 张方敏, 黄进, 等. 1961—2018年内蒙古6级及以上大风日数时空变化特征[J]. 干旱区地理, 2021, 44(5): 1290-1298.

佟新元. 考虑风电高风险爬坡事件的电力系统运行风险评估与备用决策[D]. 济南: 山东大学, 2016.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

柳天虹

齐胜利

裔扬

菅利彬

乔显著

章恩泽

王文

黎隆基

Related Institution

Key Laboratory of Modern Power System Simulation Control and New Green Energy Technology of Ministry of Education （Northeast Electric Power University）, Jilin 132012, China

School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Faulty of Automation, Huaiyin Institute of Technology, Huaian 223003, China

Key Laboratory of Thermo-Fluid Science & Engineering of MOE, Xi’an Jiaotong University, Xi’an 710049, China

School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China

AI问答

⁰