Scenario Generation for Day-ahead Wind Power Output Sequences Considering Statistical and Temporal Characteristics

YOU Zuowei; LIANG Xinyi; LIN Lingxue; XUAN Peizheng; CHENG Lanfen; ZHU Jisong

doi:10.19725/j.cnki.1007-2322.2024.0191

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Scenario Generation for Day-ahead Wind Power Output Sequences Considering Statistical and Temporal Characteristics

更新时间：2026-10-19

- Scenario Generation for Day-ahead Wind Power Output Sequences Considering Statistical and Temporal Characteristics
- Vol. 43, Issue 5, Pages: 1003-1011(2026)
- 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：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0191
  CLC： 5
- Published：2026
- 稿件说明：
移动端阅览
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：

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.

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references

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