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Sampling Strategies for Representative Time Series in Load Flow Calculations

机译:潮流计算中代表性时间序列的采样策略

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Power system analysis algorithms increasingly use time series with a high temporal resolution to assess operational and planning aspects of the power grid. By using time series with high temporal resolution, information is getting more detailed, but at the same time, the computational costs of the algorithms increase. With the help of our algorithm, we create representative time series that have similar characteristics to the original time series. With the help of these representative time series, it is possible to reduce the computational cost of power system analysis algorithms having nearly the same results as with the original time series. In this work, we improve our previous algorithm with the help of specialized sampling strategies. Furthermore, we provide a new method to compare power analysis results achieved with the representative time series to the original time series.
机译:电力系统分析算法越来越多地使用具有高时间分辨率的时间序列来评估电网的运行和规划方面。通过使用具有高时间分辨率的时间序列,信息变得更加详细,但是同时,算法的计算成本也增加了。借助我们的算法,我们创建了具有代表性的时间序列,这些时间序列具有与原始时间序列相似的特征。借助于这些代表性的时间序列,可以减少具有与原始时间序列几乎相同的结果的电力系统分析算法的计算成本。在这项工作中,我们借助专门的采样策略改进了先前的算法。此外,我们提供了一种新方法,可将具有代表性时间序列的功率分析结果与原始时间序列进行比较。

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