考虑光伏发电功率波动性的AGC备用容量分析方法

CHEN Xiaoxiao

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考虑光伏发电功率波动性的AGC备用容量分析方法

更新时间：2025-08-13

- 考虑光伏发电功率波动性的AGC备用容量分析方法
- Automation of Electric Power Systems Vol. 39, Issue 22, Pages: 16-21(2015)
- 作者机构：
  
  华北电力大学电力市场研究所,北京市,102206
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2015
- 稿件说明：
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CHEN Xiaoxiao. [J]. Automation of electric power systems, 2015, 39(22): 16-21.
DOI：

CHEN Xiaoxiao. [J]. Automation of electric power systems, 2015, 39(22): 16-21. DOI：

摘要

提出了基于自回归模型的频谱分析方法来分析光伏发电功率的波动特性。为获取足够分辨率的数据

以美国可再生能源实验室(NREL)的气象数据模拟仿真光伏阵列的出力

并用于分析不同时间尺度和空间尺度的光伏发电站的波动特性

确定光伏发电波动分量集中的时间尺度。研究表明

不同时间尺度的光伏发电波动量受天气影响显著

且随着光伏电站的规模扩大

不同光伏电站的整体出力也趋于平滑;此外

光伏发电波动主要集中在5 min的时间尺度。在此基础上

验证了采用t分布拟合关键时间尺度光伏发电波动量的概率分布效果良好

通过滚动平均法分离关键时间尺度的功率波动分量

并量化分析光伏发电并网引发的自动发电控制(AGC)备用需求。

Abstract

A spectrum analyzing method based on the autoregressive (AR) model is proposed to analyze the fluctuation characteristics of photovoltaic (PV) power. In order to obtain data with sufficient resolution

the PV array output is simulated based on meteorological data from the National Renewable Energy Laboratory (NREL) of America

which is used to analyze the fluctuation characteristics of PV power stations on different time scales and spatial scales

and determining the time scale on which the photovoltaic power fluctuation component is concentrated. Research shows that PV power fluctuations on different time scales are significantly affected by weather. With the expansion of PV power station scale

the whole output of different PV power stations tends to be smooth. In addition

PV power output fluctuation is mainly concentrated on the 5 min time scale. On this basis

the performance of fitting the probability distribution of PV power fluctuation on the critical time scale with t-distribution is verified. The power fluctuation component on the critical time scale is separated by a rolling average method. Finally

the automatic generation control (AGC) reserve demand caused by PV power grid-connection is quantitatively analyzed. This work is supported by State Grid Corporation of China (No. DZ71-13-038) and National Key Technologies R&D Program (No. 2013BAA02B01).

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