A key input to energy models are assumptions about the flexibility of power generation units, i.e., how quickly and often they can start up. These assumptions are usually calibrated on the technical characteristics of the units, such as installed capacity or technology type. However, even if power generation units technically can dispatch flexibly, service obligations and market incentives may constrain their operation. Here, we cluster over 60% of German national gas generation (generation units of 100 MWp or above) based on their empirical flexibility. We process the hourly dispatch of sample units between 2019 and 2023 using a novel deep learning approach, that transforms time series into easy-to-cluster representations. We identify two clusters of peaker units and two clusters of non-peaker units, whose different empirical flexibility is quantified by cluster-level ramp rates. Non-peaker units, around half of the sample, are empirically less flexible than peakers, and make up for more than 83% of sample must-run generation. Regulatory changes addressing the low market responsiveness of non-peakers are needed to unlock their flexibility.
View on arXiv@article{bassini2025_2504.16943,
title={ Flexibility of German gas-fired generation: evidence from clustering empirical operation },
author={ Chiara Fusar Bassini and Alice Lixuan Xu and Jorge Sánchez Canales and Lion Hirth and Lynn H. Kaack },
journal={arXiv preprint arXiv:2504.16943},
year={ 2025 }
}