考虑统计特性和时序特性的日前风电出力序列场景生成方法

尤作为; 梁歆怡; 林凌雪; 禤培正; 程兰芬; 朱继松

doi:10.19725/j.cnki.1007-2322.2024.0191

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

考虑统计特性和时序特性的日前风电出力序列场景生成方法

新能源电力系统 | 更新时间：2026-10-19

- 考虑统计特性和时序特性的日前风电出力序列场景生成方法
- Scenario Generation for Day-ahead Wind Power Output Sequences Considering Statistical and Temporal Characteristics
- 现代电力 2026年43卷第5期页码：1003-1011
- 作者机构：
  
  1. 华南理工大学电力学院,广东省,广州市,510640
  2. 南方电网科学研究院,广东,广州,510080
- 作者简介：
  
  尤作为(1999)，男，硕士研究生，研究方向为电力系统优化运行，E-mail：202221015464@mail.scut.edu.cn
  梁歆怡(2000)，女，硕士研究生，研究方向为电力系统优化运行，E-mail：epliangxy@mail.scut.edu.cn
  林凌雪(1979)，女，博士，讲师，通信作者，研究方向为电力系统运行分析与控制、电力系统稳定与控制、配电自动化系统规划等，E-mail：linlx@scut.edu.cn
  禤培正(1990)，男，硕士，工程师，研究方向为电力市场仿真模拟技术，E-mail：xuanpz@csg.cn
  程兰芬(1987)，女，硕士，高级工程师，研究方向为电力市场运营技术、电价分析技术，E-mail：chenglf@csg.cn
  朱继松(1991)，男，博士，工程师，研究方向为电力市场运营技术、电价分析技术，E-mail：zhujs1@csg.cn
- 基金信息：
  
  国家自然科学基金项目(U22B6007)。
- DOI：10.19725/j.cnki.1007-2322.2024.0191
  中图分类号： 5
- 纸质出版：2026
- 稿件说明：
移动端阅览
尤作为, 梁歆怡, 林凌雪, 等. 考虑统计特性和时序特性的日前风电出力序列场景生成方法[J]. 现代电力, 2026,43(5):1003-1011.

YOU Zuowei, LIANG Xinyi, LIN Lingxue, et al. Scenario Generation for Day-ahead Wind Power Output Sequences Considering Statistical and Temporal Characteristics[J]. 2026, 43(5): 1003-1011.
尤作为, 梁歆怡, 林凌雪, 等. 考虑统计特性和时序特性的日前风电出力序列场景生成方法[J]. 现代电力, 2026,43(5):1003-1011. DOI： 10.19725/j.cnki.1007-2322.2024.0191.

YOU Zuowei, LIANG Xinyi, LIN Lingxue, et al. Scenario Generation for Day-ahead Wind Power Output Sequences Considering Statistical and Temporal Characteristics[J]. 2026, 43(5): 1003-1011. DOI： 10.19725/j.cnki.1007-2322.2024.0191.

摘要

随着风电渗透率不断提高，传统日前调度模型难以满足电力系统“保消纳、保供电”的要求，通过风电场景集有效描述风电功率的不确定性愈发重要。针对现有统计学方法难以兼顾风电的“纵向”波动性和“横向”时序性，提出一种考虑统计特性和时序特性的日前风电出力序列场景生成方法。首先，构建基于贝叶斯优化的预测箱划分方法，通过核密度估计得到不同预测出力水平下预测误差的概率分布，同时基于马尔科夫链对预测误差的时序特性进行建模。其次，考虑风电历史波动特征，动态更新预测误差状态转移矩阵，并结合预测误差分布设计滚动抽样模式，逐时刻更新抽样概率区间，在预测误差分布中抽样组成场景。最后，以实际风电数据对所提方法进行测试，结果表明所提方法能得到准确且符合时序相关性的风电出力序列场景。

Abstract

With the continue increase in the penetration rate of wind power

traditional day-ahead dispatch models struggle to meet the requirements for guaranteeing both consumption and power supply of the power system. Effectively characterizing the uncertainty of wind power through wind power scenario sets is becoming increasingly important. Aiming at the difficulty of existing statistical methods in balancing both the volatility and temporal characteristics of wind power

we propose a method for generating day-ahead wind power output scenarios that considers both statistical and temporal characteristics. First

a prediction interval partitioning method is constructed based on Bayesian optimization. Kernel density estimation is used to obtain the probability distribution of prediction errors across different predicted output levels

while the temporal characteristics of prediction errors are modeled using Markov chains. Secondly

considering the historical volatility characteristics of wind power

the state transition matrix of prediction errors is dynamically updated. A rolling sampling model is designed by integrating the prediction error distribution

which updates the sampling probability interval at each step and generates scenarios by sampling from the prediction error distribution. Finally

the proposed method is tested with actual wind power data. The simulation result demonstrates that the method is capable of generating accurate and time-correlated wind power output scenarios.

关键词

Keywords

references

ZHANG Zhigang, KANG Chongqing. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806−2819(in Chinese).

HE Zhiyuan, YANG Jie, WU Yanan, et al . Investigation on the future AC and DC combined operation form and development trend under energy transition[J ] . Proceedings of the CSEE, 2023, 43(1): 99−114(in Chinese).

LI Bo, CHEN Minyou, ZHONG Haiwang, et al . A review of long-term planning of new power systems with large share of renewable energy[J ] . Proceedings of the CSEE, 2023, 43(02): 555−581(in Chinese).

LI Jinghua, XIE Yutian, ZENG Hongyu, et al . Research review of uncertain optimal scheduling and its application in new-type power systems[J ] . High Voltage Engineering, 2022, 48(9): 3447−3464(in Chinese).

CHEN P, PEDERSEN T, BAK-JENSEN B, et al . ARIMA-Based time series model of stochastic wind power generation[J ] . IEEE Transactions on Power Systems, 2010, 25(2): 667−676.

ZOU Jin, ZHU Jizhong, LAI Xu, et al . Simulation of wind power output series based on space-time auto-regressive moving average model[J ] . Automation of Electric Power Systems, 2019, 43(3): 101−107(in Chinese).

LUO Gang, SHI Dongyuan, CHEN Jinfu, et al . A Markov chain Monte Carlo method for simulation of wind and solar power time series[J ] . Power System Technology, 2014, 38(2): 321−327(in Chinese).

YE Lin, LI Jiachen, LU Peng, et al . Wind power time series aggregation approach based on affinity propagation clustering and MCMC algorithm[J ] . Proceedings of the CSEE, 2020, 40(12): 3744−3754(in Chinese).

ZHU Chenxi, ZHANG Yan, YAN Zheng, et al . A wind power time series modeling method based on the improved Markov Chain Monte Carlo method[J ] . Transactions of China Electrotechnical Society, 2020, 35(3): 577−589(in Chinese).

LÜ Yan, LI Li, SUN Lue, et al . Typical source-load temporal scenario generation method based on modification of sampling probability interval[J ] . Automation of Electric Power Systems, 2024, 48(17): 141-150(in Chinese).

WANG Songyan, YU Jilai, LI Haifeng, et al . A wind speed modeling method for multiple wind farms considering correlation and statistical characteristics[J ] . Automation of Electric Power Systems, 2013, 37(6): 18−23+83(in Chinese).

YANG Tian, WANG Jingbo , SONG Shaoshuai, et al . Dynamic economic dispatch of power system considering the correlation of the wind speed[J ] . Transactions of China Electrotechnical Society, 2016, 31(16): 189−197(in Chinese).

ZHAO Shuqiang, JIN Tianran, LI Zhiwei, et al . Wind power scenario generation for multiple wind farms considering temporal and spatial correlations[J ] . Power System Technology, 2019, 43(11): 3997−4004(in Chinese).

DUAN Simo, MIAO Shihong, LI Lixing, et al . Uncertainty model of combined output for multiple wind farms considering dynamic correlation of prediction errors[J ] . Automation of Electric Power Systems, 2019, 43(22): 31−37(in Chinese).

LI Jinghua, WEN Jinyu, CHENG Shijie, et al . A scene generation method considering Copula correlation relationship of multi-wind farms power[J ] . Proceedings of the CSEE, 2013, 33(16): 30−36+21(in Chinese).

XU Yechi, YAN Yunsong, ZHANG Junfang, et al . Stochastic optimal dispatching considering prediction error and frequency response[J ] . Power System Technology, 2020, 44(10): 3663−3671(in Chinese).

YAO Jinming, ZHAO Shuqiang, WEI Ziyu, et al . Day-ahead dispatch and its fast solution method of power system based on scenario analysis[J ] . El ectric Power Automation Equipment, 2022, 42(9): 102−110(in Chinese).

WANG Shouxiang, CHEN Haiwen, LI Xiaoping, et al . Conditional variational automatic encoder method for stochastic scenario generation of wind power and photovoltaic system[J ] . Power System Technology, 2018, 42(6): 1860−1869(in Chinese).

CHEN Y, WANG Y, KIRSCHEN D, et al . Model-free renewable scenario generation using generative adversarial networks[J ] . IEEE Transactions on Power Systems, 2018, 33(3): 3265−3275.

DONG Xiaochong, SUN Yingyun, PU Tianjiao. Day-ahead scenario generation of renewable energy based on conditional GAN[J]. Proceedings of the CSEE, 2020, 40(17): 5527−5536(in Chinese).

HUANG Yuehui, SUN Yanan, LI Chi, et al . Constructing method of short-term output scenarios for multi-regional wind power based on conditional generative adversarial network[J ] . Power System Technology, 2023, 47(1): 63−77(in Chinese).

GB/T 40607—2021. 调度侧风电或光伏功率预测系统技术要求[S].

BLUDSZUWEIT H, DOMINGUEZ-NAVARRO J A, LLOMBART A. Statistical analysis of wind power forecast error[J]. IEEE Transactions on Power Systems, 2008, 23(3): 983−991.

YANG Nan, HUANG Yu, YE Di, et al . Study on probability distribution of wind power fluctuation based on NACEMD and improved nonparametric kernel density estimation[J ] . Power System Technology, 2019, 43(3): 910−917(in Chinese).

YANG Xian, WANG Liubing, LI Delin. A wind power scenarios analysis method based on adaptive prediction box[J]. Power System and Clean Energy, 2020, 36(8): 82−90(in Chinese).

CUI Jiaxu, YANG Bo. Survey on Bayesian optimization methodology and applications[J]. Journal of Software, 2018, 29(10): 3068−3090(in Chinese).

李东风. 现代统计学系列丛书统计计算[M]. 北京: 高等教育出版社, 2017.

XIN Kuo, MA Qian, XU Qin, et al . Wind power output sequence scenario generation method based on monthly division and specified day type[J ] . Automation of Electric Power Systems, 2023, 47(15): 151−161(in Chinese).

浏览量

270

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于改进生成对抗网络生成风光场景的微电网多时间尺度优化调度策略研究

基于长短期记忆神经网络组合算法的短期电力负荷预测

基于增强型去噪自编码器与随机森林的电力系统扰动分类方法

基于多粒度柔性负荷特性的多时间尺度混合电价型需求响应

基于最小二乘法–遗传算法和改进可变模糊集的电能质量综合评估