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

Kruse, Johannes; Schäfer, Benjamin; Witthaut, Dirk

doi:10.1016/j.patter.2021.100365

Cited by 38 publications

(52 citation statements)

References 42 publications

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“…We refer to ref. [9] for a detailed description of the data set, including the processing and the aggregation of the external features.…”

Section: B Data Sources and Preparationmentioning

confidence: 99%

“…We apply the explainable ML model from ref. [9] to explore the impact of external features on the DFDs. The model uses a Gradient Tree Boosting model [22] for the prediction of hourly RoCoFs from external features, which is then explained through SHAP values.…”

Section: Machine Learning Model For Hourly Rocofmentioning

confidence: 99%

“…to the slope of the load curve [4], [6], [7] and a connection between large frequency events and the load ramp is suggested [3], [8]. However, not only load ramps but many other external features such as electricity prices or forecasting errors affect power grid frequency deviations [9].…”

Section: Introductionmentioning

confidence: 99%

“…Due to control measures, frequency dynamics exhibit complex non-linear dependencies [1] and measurement errors further modify many publicly available grid frequency measurements [10]. Moreover, drivers of frequency deviations such as load or generation ramps are strongly correlated [9]. Modern ML methods, such as Gradient Tree Boosting, are able to extract non-linear dependencies even from noisy data with correlated features [11], which is not possible in a simple correlation analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we explore hourly DFDs in the Continental Euro-pean (CE) synchronous area with an explainable ML model introduced in ref. [9]. In sec.…”

Section: Introductionmentioning

confidence: 99%

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Exploring deterministic frequency deviations with explainable AI

Kruse,

Schäfer,

Witthaut

2021

Preprint

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Deterministic frequency deviations (DFDs) critically affect power grid frequency quality and power system stability. A better understanding of these events is urgently needed as frequency deviations have been growing in the European grid in recent years. DFDs are partially explained by the rapid adjustment of power generation following the intervals of electricity trading, but this intuitive picture fails especially before and around noonday. In this article, we provide a detailed analysis of DFDs and their relation to external features using methods from explainable Artificial Intelligence. We establish a machine learning model that well describes the daily cycle of DFDs and elucidate key interdependencies using SHapley Additive exPlanations (SHAP). Thereby, we identify solar ramps as critical to explain patterns in the Rate of Change of Frequency (RoCoF).

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“…We refer to ref. [9] for a detailed description of the data set, including the processing and the aggregation of the external features.…”

Section: B Data Sources and Preparationmentioning

confidence: 99%

Section: Machine Learning Model For Hourly Rocofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Here, we explore hourly DFDs in the Continental Euro-pean (CE) synchronous area with an explainable ML model introduced in ref. [9]. In sec.…”

Section: Introductionmentioning

confidence: 99%

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Exploring deterministic frequency deviations with explainable AI

Kruse,

Schäfer,

Witthaut

2021

Preprint

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The anticipatory paradigm

2023

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Anticipatory thinking is necessary for managing risk in the safety-and missioncritical domains where AI systems are being deployed. We analyze the intersection of anticipatory thinking, the optimization paradigm, and metaforesight to advance our understanding of AI systems and their adaptive capabilities when encountering low-likelihood/high-impact risks. We describe this intersection as the anticipatory paradigm. We detail these challenges in concrete examples and propose new types of anticipatory thinking, towards a paradigm shift in how AI systems are evaluated.

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Artificial intelligence-based solutions for climate change: a review

Chen

Zhang

et al. 2023

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Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.

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

Cited by 38 publications

References 42 publications

Exploring deterministic frequency deviations with explainable AI

Exploring deterministic frequency deviations with explainable AI

The anticipatory paradigm

Artificial intelligence-based solutions for climate change: a review

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