Comparison of different methods of spatial disaggregation of electricity

TL;DR

This paper compares various spatial disaggregation methods for electricity data, highlighting how data resolution and approach type significantly influence regionalization outcomes in energy system models.

Contribution

It introduces a methodology to evaluate regionalization techniques for renewable energy and load data without requiring detailed grid topology knowledge.

Findings

Data resolution greatly impacts regionalization accuracy.

Top-down and bottom-up approaches yield different regionalization results.

The proposed evaluation method simplifies comparison of regionalization workflows.

Abstract

Energy system models involve various input data sets representing the generation, consumption and transport infrastructure of electricity. Especially energy system models with a focus on the transmission grid require time series of electricity feed-in and consumption in a high spatial resolution. In general, there are two approaches to obtain regionalized time series: top-down and bottom-up. In many cases, both methodologies may be combined to aggregate or disaggregate input data. Furthermore, there exist various approaches to assign regionalized feed-in of renewable energy sources and electrical load to the model's grid connection points. The variety in the regionalization process leads to significant differences on a regional scope, even if global values are the same. We develop a methodology to compare regionalization techniques of input data for photovoltaics, wind and electrical load between various models as well as data assignment techniques to the power grid nodes. We further define two invariants to evaluate the outcome of the regionalization process at the NUTS 3 level, one invariant for the annual profiles and one for the installed capacities. This methodology enabled us to compare different regionalization and assignment workflows using simple parameters, without explicit knowledge of grid topology. Our results show that the resolution of the input data and the use of a top-down or a bottom-up approach are the most determinant factors in the regionalization process.