Enhancing Energy System Models Using Better Load Forecasts

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Date

21 février 2023

Type de document
Périmètre
Identifiant
  • 2302.11017
Collection

arXiv

Organisation

Cornell University




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Thomas Möbius et al., « Enhancing Energy System Models Using Better Load Forecasts », arXiv - économie


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Energy system models require a large amount of technical and economic data, the quality of which significantly influences the reliability of the results. Some of the variables on the important data source ENTSO-E transparency platform, such as transmission system operators' day-ahead load forecasts, are known to be biased. These biases and high errors affect the quality of energy system models. We propose a simple time series model that does not require any input variables other than the load forecast history to significantly improve the transmission system operators' load forecast data on the ENTSO-E transparency platform in real-time, i.e., we successively improve each incoming data point. We further present an energy system model developed specifically for the short-term day-ahead market. We show that the improved load data as inputs reduce pricing errors of the model, with strong reductions particularly in times when prices are high and the market is tight.

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