半月刊

ISSN 1000-1026

CN 32-1180/TP

+高级检索 English
基于修正晴空模型的超短期光伏功率预测
作者:
作者单位:

1.清华大学电机工程与应用电子技术系,北京市 100084;2.电力系统及大型发电设备安全控制和仿真国家重点实验室,清华大学,北京市 100084

摘要:

文中旨在提高晴空或有薄云这类小波动场景下的光伏功率超短期预测精度。虽然,太阳辐射的日周期性和年周期性使光伏功率序列具有确定性分量,但是电站和当地气象的详细参数随时间变化且难以获取。为此,提出一个仅依赖少量参数的改进晴空功率计算模型,并在此基础上构建了在线更新参数的预测算法,预测小波动天气下光伏电站未来4 h的功率。采用中国吉林省某电站的数据进行了测试,结果表明:所提模型得到的晴空功率曲线可以较准确地拟合小波动天气下的电站出力,而基于在线更新参数的光伏预测结果可以使小波动天气第4个小时的预测误差降低到约3.78%,弥补了相邻晴天方法在一些场景下误差超过5%甚至达到10%的不足。文中所提方法不仅可以提高小波动天气下光伏功率超短期预测精度,也为复杂天气条件下的预测提供了更准确的基准值。

关键词:

基金项目:

国家重点研发计划资助项目(2018YFB0904200);国家电网公司科技项目(SGLNDKOOKJJS1800266)。

通信作者:

作者简介:


Ultra-short-term Photovoltaic Power Prediction Based on Modified Clear-sky Model
Author:
Affiliation:

1.Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;2.State Key Laboratory of Power System and Generation Equipment, Tsinghua University, Beijing 100084, China

Abstract:

This paper aims to improve the accuracy of ultra-short-term prediction of photovoltaic power in small fluctuation scenarios such as the clear sky or thin cloud. Although, the diurnal and annual periodicity of solar radiation makes the photovoltaic power series have deterministic components, the detailed parameters of power stations and local weather change with time and are difficult to obtain. To this end, this paper proposes a modified clear-sky power calculation model that only depends on a few parameters. On this basis, a prediction algorithm updating parameters online is constructed to predict the power of a photovoltaic power station in the next four hours under the small fluctuation weather. The test is performed by using data from a power station in Jilin province. The results show that the clear-sky power curve obtained by the proposed model can accurately fit the power station output under the small fluctuation weather. The photovoltaic prediction results based on the updating parameters online can reduce the prediction error to about 3.78% in the fourth hour under small fluctuation weather, which makes up for the deficiency that the error of adjacent sunny method is more than 5% or even 10% in some scenarios. The proposed method not only improves the accuracy of ultra-short-term prediction of photovoltaic power under small fluctuation weather but also provides a more accurate stabilization benchmark for complex weather conditions.

Keywords:

Foundation:
This work is supported by National Key R&D Program of China (No. 2018YFB0904200) and State Grid Corporation of China (No. SGLNDKOOKJJS1800266).
引用本文
[1]马原,张雪敏,甄钊,等.基于修正晴空模型的超短期光伏功率预测[J/OL].电力系统自动化,http://doi. org/10.7500/AEPS20200227003.
MA Yuan, ZHANG Xuemin, ZHEN Zhao, et al. Ultra-short-term Photovoltaic Power Prediction Based on Modified Clear-sky Model[J/OL]. Automation of Electric Power Systems, http://doi. org/10.7500/AEPS20200227003.
复制
分享
历史
  • 收稿日期:2020-02-27
  • 最后修改日期:2020-12-25
  • 录用日期:2020-11-02
  • 在线发布日期:
  • 出版日期:
相关附件