计及爬坡特征的风电年度出力序列建模方法

张锐; 陈皓轩; 张粒子; 黄弦超; 王运; 田宏杰

doi:doi:10.19912/j.0254-0096.tynxb.2024-0917

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

计及爬坡特征的风电年度出力序列建模方法

更新时间：2025-12-17

- 计及爬坡特征的风电年度出力序列建模方法
- Issue 10, Pages: 570-580(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0917
  CLC： M614
- Published Online：31 October 2025，
  
  Published：31 October 2025
- 稿件说明：
移动端阅览
张锐, 陈皓轩, 张粒子, et al. 计及爬坡特征的风电年度出力序列建模方法[J]. 2025, (10): 570-580.
DOI：

张锐, 陈皓轩, 张粒子, et al. 计及爬坡特征的风电年度出力序列建模方法[J]. 2025, (10): 570-580. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0917.

摘要

针对新型电力系统下风电爬坡事件频发导致系统长期灵活调节容量充裕性不足的问题

提出一种计及爬坡特征的风电年度出力序列建模方法

提高中长期时间尺度下系统灵活性评估精度

以期指导灵活性资源的规划与建设。首先

构建风电日出力爬坡特征指标

通过旋转门算法与四点法筛选提取风电爬坡功率片段以实现爬坡特征指标的量化计算

应用K-medoids方法与核密度估计构建风电日出力爬坡特征指标概率分布模型;然后

通过马尔科夫链蒙特卡洛算法（MCMC）模拟气象条件对风电日出力序列间转移特性的影响

通过蒙特卡洛抽样与出力序列优化得到兼具风电出力爬坡特征与时序特征的风电年出力序列;最后

基于中国西北某省风电出力数据进行算例仿真计算

利用生成的风电年度出力序列进行系统长期灵活性评估

验证所提方法的有效性与实用性。

Abstract

To address the issue of inadequate long-term flexible adjustment capacity caused by the frequent occurrence of wind power ramping events in the new power system

a modeling method for the annual output sequence of wind power considering the ramping characteristics is proposed. This method enhances the precision of system flexibility assessment over medium- and long-term time horizons

thereby providing guidance for the planning and development of flexibility resources. Initially

wind power ramping segments are meticulously filtered and extracted using the Spinning Door algorithm and the Four-Point method. Subsequently

the daily wind power output ramping characteristic indices are identified by analyzing the morphology and distribution properties of these ramping segments. With these indices in hand

the -medoids clustering algorithm and kernel density estimation are employed to establish a probabilistic distribution model for the daily wind power output ramping features. This enables the generation of a daily wind power output sequence that encapsulates both ramping characteristics and time series attributes

achieved through Monte Carlo sampling and sequence optimization techniques. In the final step

the Markov Chain Monte Carlo （MCMC） method is utilized to simulate the impact of meteorological conditions on the transition characteristics between daily wind power output sequences

thereby creating annual wind power output sequences that accurately reflect both the stochastic nature and ramping characteristics of wind power. The simulation is grounded in wind power output data from a province in Northwest China. The resultant annual wind power output sequences are then applied to assess the long-term flexibility of the system

validating the efficacy and practical applicability of the proposed methodology.

关键词

Keywords

references

《新型电力系统发展蓝皮书》编写组.新型电力系统发展蓝皮书[M]. 北京: 中国电力出版社, 2023, 16-17.

NAVID N, ROSENWALD G.Market solutions for managing ramp flexibility with high penetration of renewable resource[J]. IEEE transactions on sustainable energy, 2012, 3(4): 784-790.

李海波, 鲁宗相, 乔颖, 等. 大规模风电并网的电力系统运行灵活性评估[J]. 电网技术, 2015, 39(6): 1672-1678.

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.

SEVLIAN R, RAJAGOPAL R.Detection and statistics of wind power ramps[J]. IEEE transactions on power systems, 2013, 28(4): 3610-3620.

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.

CUI M J, ZHANG J, FLORITA A R, et al.An optimized swinging door algorithm for identifying wind ramping events[J]. IEEE transactions on sustainable energy, 2016, 7(1): 150-162.

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.

ZHAO Y C, ZHU W L, YANG M, et al.Bayesian network based imprecise probability estimation method for wind power ramp events[J]. Journal of modern power systems and clean energy, 2021, 9(6): 1510-1519.

乔妍, 韩丽, 李梦洁. 基于爬坡特征和云模型的风电功率预测误差区间评估[J]. 电力系统自动化, 2022, 46(11): 75-84.

李辉, 任洲洋, 胡博, 等. 基于时序生成对抗网络的月度风光发电功率场景分析方法[J]. 中国电机工程学报, 2022, 42(2): 537-548.

辛阔, 马骞, 许琴, 等. 基于月份划分与指定日类型的风电出力序列场景生成方法[J]. 电力系统自动化, 2023, 47(15): 151-161.

丁明, 解蛟龙, 刘新宇, 等. 面向风电接纳能力评价的风资源/负荷典型场景集生成方法与应用[J]. 中国电机工程学报, 2016, 36(15): 4064-4072.

于鹏, 黎静华, 文劲宇, 等. 含风电功率时域特性的风电功率序列建模方法[J]. 中国电机工程学报, 2014, 34(22): 3715-3723.

董骁翀, 张姝, 李烨, 等. 电力系统中时序场景生成和约简方法研究综述[J]. 电网技术, 2023, 47(2): 709-721.

LI J H, ZHOU J S, CHEN B.Review of wind power scenario generation methods for optimal operation of renewable energy systems[J]. Applied energy, 2020, 280: 115992.

曲凯, 李湃, 黄越辉, 等. 面向新能源消纳能力评估的年负荷序列建模及场景生成方法[J]. 电力系统自动化, 2021, 45(1): 123-131.

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.

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.

LI D, YAN W, LI W Y, et al.A two-tier wind power time series model considering day-to-day weather transition and intraday wind power fluctuations[J]. IEEE transactions on power systems, 2016, 31(6): 4330-4339.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

时间嵌入和注意力机制的超短期风速预测混合模型研究

构网型双馈风电机组稳定性分析

计及制氢效率的风电制氢系统优化控制策略

基于电站聚类和TCN-QR-KDE的省级电网新能源非参数概率预测方法

Related Author

平悦

单永娟

李诚

田建辉

黄国勇

邓为权

刘发炳

颜湘武

Related Institution

School of Electrical Engineering and Automation, Hefei University of Technology

Anhui Province Key Laboratory of Renewable Power Utilization and Energy Conservation

Institute of Energy & Environment Science,Shantou University

Department of Thermal Engineering,Tsinghua University

汕头大学能源与环境科学研究所汕头515063

AI问答

⁰