EURO 2024 Copenhagen
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1920. Applying Data-Driven Robustness for Weather year uncertainty to generate Robust Energy Systems

Invited abstract in session TA-14: Enhanced statistical methods for energy challenges, stream Energy Markets.

Tuesday, 8:30-10:00
Room: 16 (building: 116)

Authors (first author is the speaker)

1. Sebastian Kebrich
Forschungszentrum Jülich

Abstract

Limiting climate change demands an extensive expansion of variable renewable energy technologies. Capacity expansion models estimate the necessary installations of these technologies. However, a major challenge of energy systems based on these technologies are interannual weather fluctuations across weather years leading to uncertainties in capacity expansion as well as operation to match supply and demand. The presented study investigates the 38 government districts of Germany using 40 years of weather data with hourly resolution to calculate energy systems able to supply energy in all cases. Based on computational experiments with weather data from multiple years, we identify critical dark lull-patterns in weather years. We showcase how such patterns can be incorporated into arbitrary reference years by manipulating time-series data. The developed methodology takes uncertainties in the capacity factor based on the weather data into account and weighs them relative to the demand. Large discrepancies between capacity factors and demand indicate time periods with potential gaps between supply and demand. The resulting energy systems based on this manipulated weather year are then taken as input for cross-validation to proof feasibility of the resulting systems. The results show that already the inclusion of only a few, well selected time periods leads to robust energy system models with minor optimality gap and can ultimately to a substantial decline in calculation time.

Keywords

Status: accepted

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