Comparison of different methods of spatial disaggregation of electricity generation and consumption time series. (July 2022) - Raventós, Oriol; Dengiz, Thomas; Medjroubi, Wided; Unaichi, Chinonso; Bruckmeier, Andreas; Finck, Rafael
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. Highlights: Methodology for data regionalization comparison for transmission models is developed. Methodology extended for comparison of input data assignment to ehv nodes. Eight different models and their regionalization workflows were compared in details. Top-down or bottom-up regionalization determine the regionalization outcome. Akin PV and load regionalization outputs due to their daily and weekly patterns.