在极端天气条件下,电网的最佳实时电力调度与风能预测
Yixin Zhuo1, Ling Li1, Jian Tang1
1Dispatching Control Center of Guangxi Power Grid, Nanning 530000, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究介绍了一种风能预测模型,以优化实时发电,改善极端天气期间的电网稳定性. 结合的ARIMA和INFO算法提高了功率控制性能.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 控制理论 控制理论
背景情况:
- 动力电子技术的进步改善了风力发电的稳定性和调节.
- 极端天气事件对实时电力系统运行和调度构成挑战.
- 当前的系统在不可预测的条件下,难以实现监管资源的最佳分配.
研究的目的:
- 提出基于风能预测的电力调度模型,优化时间控制间隔.
- 提高风能系统实时发电的稳定性和效率.
- 在不利的条件下,尽量减少命令和实际输出之间的功率偏差.
主要方法:
- 使用自回归集成移动平均线 (ARIMA) 模型来分析极端天气造成的风能中断.
- 使用其他监管资源来补偿风力发电损失和系统不匹配.
- 实现矢量加权平均值 (INFO) 算法,以实时优化功率调度.
主要成果:
- 拟议的模型有效地使用ARIMA分析了极端天气下的风能中断.
- INFO算法成功地将功率偏差最小化,确保更好的实时功率调度.
- 模拟结果显示,功率调度-风能预测系统 (PD-WEF) 的功率控制性能得到了改进.
结论:
- 集成ARIMA用于预测和INFO用于调度优化,可以显著提高风能系统的功率控制.
- 拟议的模型为在极端天气挑战中稳定发电提供了强大的解决方案.
- 这种方法有助于使风力发电更可靠,更有效地融入电网.
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