基于分段优化的风电功率爬坡有限度控制策略

DU Bin

您当前的位置：

首页 >

文章列表页 >

基于分段优化的风电功率爬坡有限度控制策略

更新时间：2025-08-13

- 基于分段优化的风电功率爬坡有限度控制策略
- Automation of Electric Power Systems Vol. 40, Issue 5, Pages: 78-82(2016)
- 作者机构：
  
  上海电机学院电气学院,上海市,201306
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
移动端阅览
DU Bin. [J]. Automation of electric power systems, 2016, 40(5): 78-82.
DOI：

DU Bin. [J]. Automation of electric power systems, 2016, 40(5): 78-82. DOI：

摘要

风电功率爬坡是一种严重的风电功率波动情况

对电网稳定性产生了严重的影响。风电功率预测能够提前预测功率爬坡事件

为制定功率爬坡控制策略提供爬坡数据。在当前风电功率爬坡的有限度控制策略基础上

提出了基于分段优化的有限度控制策略。根据风电功率爬坡过程中爬坡率的相对大小

对爬坡过程进行合理分段。最小优化目标函数由爬坡率和弃风量构成。通过在不同区段上设置不同的权重因子来调整分段优化目标

利用二次规划算法最后得出功率参考曲线。仿真结果表明

该功率参考曲线降低了爬坡率、减少了弃风量。

Abstract

As a serious wind power fluctuation

wind power ramping has a serious impact on the power systems stability. Wind power forecast can predict power ramping in advance

so it can provide data for the power ramping control strategy. Based on the finite control strategy of current wind power ramping

a finite control strategy based on segmentation optimization is proposed. It divides the ramping process into reasonable segments according to the relative size of the wind power ramp rate. The minimum objective function includes wind power ramp rate and abandoned air volume. It is adjusted by setting different weight factors on different segments

and the power reference curve is obtained by using quadratic programming algorithm. The simulation results show that the wind power ramp rate and abandoned air volume can be decreased by the power reference curve. This work is supported by National Natural Science Foundation of China (No. 11304200)

Natural Science Foundation of Shanghai (No. 15ZR1417300

No. 14ZR1417200)

and Innovation Program of Shanghai Municipal Education Commission (No. 14YZ157

No. 15ZZ106).

关键词

Keywords

references

Views

4481

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Wind Power Interval Prediction Based on Fluctuation Trend Segmentation

Multi-time-scale Dispatching of Integrated Electricity and Thermal System Considering Time-sharing Compensation of Wind Power Forecasting Error

Ultra-short-term Probabilistic Forecasting of Wind Power Based on Temporal Mixture Density Network

Interval Estimation of Wind Power Forecasting Error Based on Ramp Features and Cloud Model

Data-driven Decoupling Evaluation Method of Wind Power Prediction Error

Related Author

WANG Xiaojing

WANG Chong

YU Hongbo

YU Xiaojiao

HAN Li

WANG Xiaojing

LU Panpan

Related Institution

School of Electrical Engineering, China University of Mining and Technology

China Electric Power Research Institute

School of Electrical and Electronics Engineering, North China Electric Power University

School of Electrical and Power Engineering, China University of Mining and Technology

⁰