This title appears in the Scientific Report :
2022
Please use the identifier:
http://dx.doi.org/10.1016/j.epsr.2022.108489 in citations.
Please use the identifier: http://hdl.handle.net/2128/31787 in citations.
Secondary control activation analysed and predicted with explainable AI
Secondary control activation analysed and predicted with explainable AI
The transition to a renewable energy system challenges 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 that a solid understanding o...
Saved in:
Personal Name(s): | Kruse, Johannes (Corresponding author) |
---|---|
Schäfer, Benjamin / Witthaut, Dirk | |
Contributing Institute: |
Systemforschung und Technologische Entwicklung; IEK-STE |
Published in: | Electric power systems research, 212 (2022) S. 108489 - |
Imprint: |
Amsterdam [u.a.]
Elsevier Science
2022
|
DOI: |
10.1016/j.epsr.2022.108489 |
Document Type: |
Journal Article |
Research Program: |
Verbundvorhaben CoNDyNet2: Kollektive nichtlineare Dynamik komplexer Stromnetze Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) Societally Feasible Transformation Pathways |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/31787 in citations.
The transition to a renewable energy system challenges 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 that a solid understanding of its predictability and driving factors is needed. Here, we establish an explainable machine learning model for the analysis of secondary control power in Germany. Training gradient boosted trees, we obtain an accurate ex-post description of control activation. Our explainable model demonstrates the strong impact of external drivers such as forecasting errors and the generation mix, while daily patterns in the reserve activation play a minor role. Training a prototypical forecasting model, we identify forecast error estimates as crucial to improve predictability. Generally, input data and model training have to be carefully adapted to serve the different purposes of either ex-post analysis or forecasting and reserve sizing. |