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001     897203
005     20240507205536.0
024 7 _ |a 10.1016/j.patter.2021.100365
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100 1 _ |a Kruse, Johannes
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245 _ _ |a Revealing drivers and risks for power grid frequency stability with explainable AI
260 _ _ |a [Amsterdam]
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520 _ _ |a Stable operation of an electric power system requires strict operational limits for the grid frequency. Fluctuations and external impacts can cause large frequency deviations and increased control efforts. Although these complex interdependencies can be modeled using machine learning algorithms, the black box character of many models limits insights and applicability. In this article, we introduce an explainable machine learning model that accurately predicts frequency stability indicators for three European synchronous areas. Using Shapley additive explanations, we identify key features and risk factors for frequency stability. We show how load and generation ramps determine frequency gradients, and we identify three classes of generation technologies with converse impacts. Control efforts vary strongly depending on the grid and time of day and are driven by ramps as well as electricity prices. Notably, renewable power generation is central only in the British grid, while forecasting errors play a major role in the Nordic grid.
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700 1 _ |a Witthaut, Dirk
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700 1 _ |a Schäfer, Benjamin
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773 _ _ |a 10.1016/j.patter.2021.100365
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856 4 _ |u https://juser.fz-juelich.de/record/897203/files/Invoice_OAD0000149406.pdf
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