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

doi:10.12688/openreseurope.13420.1

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

2021-04-19

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DOI：10.12688/openreseurope.13420.1

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Introduction: Data processing is a crucial step in energy system modelling which prepares input data from various sources into a format needed to formulate a model. Multiple open-source web-hosted databases offer pre-processed input data within the European context. However, the number of documented open-source data processing workflows that allow for the construction of energy system models with specified spatial resolution reduction methods is still limited. Methods: This paper presents a novel data processing approach to construct sector-coupled energy system models for European countries while maximising the use of existing web-hosted pre-processed data. Three power and heat optimisation models of Germany were constructed using different spatial resolution reduction methods. Results: Significant variation in generation, transmission and storage capacity of electricity were observed between the optimisation results of the energy system models. The results of the model that used administrative state boundaries to define regions were found to be sensitive to the omission of solar rooftop photovoltaic availability. Conclusions: This paper uses the proposed data processing approach to demonstrate the importance of spatial context when building and analysing power and heat optimisation models.

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

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