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A data processing approach with built-in spatial resolution reduction methods to construct energy system models.

Christian Etienne Fleischer1

  • 1Department of Energy and Environmental Management, Europa-Universität Flensburg, Flensburg, 24943, Germany.

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Summary
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This study introduces a new data processing method for creating energy system models. It enables spatial aggregation, crucial for accurate energy system modeling with open-source tools.

Keywords:
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Area of Science:

  • Energy Systems Analysis
  • Computational Modeling
  • Data Science

Background:

  • Energy system modeling requires processed input data from diverse sources.
  • Open-source databases provide European energy data, but documented processing workflows are limited.
  • Spatial resolution reduction methods are essential for constructing energy system models.

Purpose of the Study:

  • To present a novel data processing approach for energy system modeling.
  • To integrate spatial aggregation methods into data processing workflows.
  • To facilitate the construction of sector-coupled energy system models.

Main Methods:

  • Utilized web-hosted pre-processed data and open-source software to build a dataset.
  • Applied a specified spatial aggregation method to the dataset.
  • Employed the aggregated dataset to construct sector-coupled energy system models.

Main Results:

  • Successfully constructed three power and heat optimization models for Germany.
  • Observed significant variations in electricity generation, transmission, and storage capacities across models.
  • Demonstrated the applicability of the data processing approach in a real-world energy system context.

Conclusions:

  • A novel data processing approach for constructing sector-coupled energy system models has been presented.
  • The method integrates spatial aggregation techniques, addressing a gap in existing workflows.
  • The approach facilitates the creation of detailed energy system models with reduced spatial resolution.