考虑tso风险规避的动态线路额定容量概率预测

Dejenie Birile Gemeda, W. Stork
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引用次数: 2

摘要

具有高概率负荷的可再生能源在新兴输电网中的高度渗透,迫使输电系统运营商(TSOs)利用智能输电网管理方法来最大限度地利用其资源。由于依赖于天气条件,架空导线的实时电容量波动很大。因此,采用动态线路额定值(DLR)比传统的保守静态额定值(取决于最坏的天气条件)更好地利用架空导线的实时额定值。由于点预测DLR电容量计算具有较高的不确定性,DLR预测方法的概率手段为短期规划和实时监测架空输电线路电容量提供了可能,从而使输电网络在不损害全网的情况下平稳运行。本研究构建实时DLR架空输电线路,采用不同分位数的QRF机器学习模型,提前24小时进行电容量预测和负荷限制。该方法为最低分位数提供了更好的增强和安全操作,以减轻决策者的风险规避。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probabilistic Ampacity Forecasting of Dynamic Line Rating Considering TSOs Risk-Averse
High penetration of renewable energy resources with highly probabilistic loading in the emerging power transmission network is forcing Transmission System Operators (TSOs) to utilize their resources to the exhaustive extent by making use of intelligent transmission network management methods. The real-time ampacity of overhead conductors is tremendously fluctuating due to its dependence on weather conditions. As a result, the real-time rating of the overhead conductor is better exploited by using dynamic line rating (DLR) than traditional conservative static rating, which depends on the worst-case weather conditions. Since there are high uncertainties associated with point forecast DLR ampacity calculation, probabilistic means of DLR forecasting method provide the possibility for short-term planning and real-time overhead transmission line ampacity monitoring, thus enabling the transmission network to run smoothly without harm to the entire network. In this study, a real-time DLR overhead transmission line is formulated, giving 24-hour ahead ampacity prediction and loading limits by using quantile regression forest (QRF) machine learning model with different quantiles. The proposed method provides better enhancement and safe operation for the lowest quantiles to mitigate decision-makers risk-averse.
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