超短期风电场电力预测 考虑到风力发电波动的相关性
Chuandong Li1, Minghui Zhang2, Yi Zhang3
1College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350100, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究引入了一种用于超短期风电场功率预测的新方法,通过分析相邻风电场之间的风力波动的空间和时间相关性来提高准确性. 该方法提高了在动态风条件下生产规划和功率平衡.
科学领域:
- 可再生能源系统可再生能源系统
- 电力系统工程 电力系统工程
- 气象学 天气学
背景情况:
- 风电场的超短期电力预测对于电网稳定性和经济运行至关重要.
- 快速的风速波动给准确的预测带来了重大挑战.
- 现有的方法往往无法充分捕捉风力发电的复杂时空动态.
研究的目的:
- 开发一个精确的风电场超短期功率预测方法.
- 结合相邻风电场之间的风力波动的空间和时间相关性.
- 改进在风力变化条件下的电力平衡和生产规划.
主要方法:
- 根据风力数据和风电场位置计算功率波动的时间差异,以确定预先信息期.
- 采用一个变化的贝叶斯模型来提取相邻风电场功率波动之间的隐性关系.
- 预测能力为前期和非前期信息期实现超短期预测.
主要成果:
- 与现有模型相比,拟议的方法证明了预测准确度的提高.
- 从相邻的风电场有效利用功率波动特征是提高性能的关键.
- 该模型显示在不同风电场地点之间有一定程度的可通用性.
结论:
- 这种新的方法成功地解决了在波动的风力条件下进行超短期风力发电预测的挑战.
- 考虑到农场间的相关性,可以显著提高预测的准确性.
- 这种方法为优化风电场运营和电网集成提供了有价值的工具.
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