Revealing the empirical flexibility of gas units through deep clustering
This work addresses the need for better flexibility modeling in energy systems to support the transition to renewables, though it is incremental as it applies a novel method to a specific domain.
The paper tackled the problem of estimating the empirical flexibility of gas power generation units from real-world electricity generation data, revealing that half of the units have low flexibility comparable to coal units, with non-peakers generating an average of 1.3 GWh during low residual load hours.
The flexibility of a power generation unit determines how quickly and often it can ramp up or down. In energy models, it depends on assumptions on the technical characteristics of the unit, such as its installed capacity or turbine technology. In this paper, we learn the empirical flexibility of gas units from their electricity generation, revealing how real-world limitations can lead to substantial differences between units with similar technical characteristics. Using a novel deep clustering approach, we transform 5 years (2019-2023) of unit-level hourly generation data for 49 German units from 100 MWp of installed capacity into low-dimensional embeddings. Our unsupervised approach identifies two clusters of peaker units (high flexibility) and two clusters of non-peaker units (low flexibility). The estimated ramp rates of non-peakers, which constitute half of the sample, display a low empirical flexibility, comparable to coal units. Non-peakers, predominantly owned by industry and municipal utilities, show limited response to low residual load and negative prices, generating on average 1.3 GWh during those hours. As the transition to renewables increases market variability, regulatory changes will be needed to unlock this flexibility potential.