Journal Article FZJ-2021-03669

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Revealing drivers and risks for power grid frequency stability with explainable AI

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2021
Elsevier [Amsterdam]

Patterns 2(11), 100365 - () [10.1016/j.patter.2021.100365]

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Abstract: 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.

Classification:

Contributing Institute(s):
  1. Systemforschung und Technologische Entwicklung (IEK-STE)
Research Program(s):
  1. 1112 - Societally Feasible Transformation Pathways (POF4-111) (POF4-111)
  2. HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) (HDS-LEE-20190612)
  3. CoNDyNet 2 - Kollektive Nichtlineare Dynamik Komplexer Stromnetze (BMBF-03EK3055B) (BMBF-03EK3055B)

Appears in the scientific report 2021
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Medline ; Medline ; Creative Commons Attribution CC BY 4.0 ; DOAJ ; OpenAccess ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; IF >= 5 ; JCR ; PubMed Central ; SCOPUS ; Web of Science Core Collection
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 Record created 2021-09-28, last modified 2024-05-07