# Secondary control activation analysed and predicted with explainable AI

arXiv: https://arxiv.org/abs/2109.04802 · HTML: https://mlsanity.com/paper/2109.04802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Revealing drivers and risks for power grid frequency stability with explainable AI](https://mlsanity.com/paper/2106.04341/) (2021)
- [Exploring deterministic frequency deviations with explainable AI](https://mlsanity.com/paper/2106.09538/) (2021)

## Built on by (most cited first, 2 of 2)

- [Understanding electricity prices beyond the merit order principle using explainable AI](https://mlsanity.com/paper/2212.04805/) (2022)
- [AI Explainability and Governance in Smart Energy Systems: A Review](https://mlsanity.com/paper/2211.00069/) (2022)

## Abstract (arXiv)

The transition to a renewable energy system poses challenges for power grid operation and stability. Secondary control is key in restoring the power system to its reference following a disturbance. Underestimating the necessary control capacity may require emergency measures, such as load shedding. Hence, a solid understanding of the emerging risks and the driving factors of control is needed. In this contribution, we establish an explainable machine learning model for the activation of secondary control power in Germany. Training gradient boosted trees, we obtain an accurate description of control activation. Using SHapely Additive exPlanation (SHAP) values, we investigate the dependency between control activation and external features such as the generation mix, forecasting errors, and electricity market data. Thereby, our analysis reveals drivers that lead to high reserve requirements in the German power system. Our transparent approach, utilizing open data and making machine learning models interpretable, opens new scientific discovery avenues.