Fleischer Christian Etienne
Department of Energy and Environmental Management, Europa-Universität Flensburg, Flensburg, 24943, Germany.
Open Res Eur. 2022 Feb 10;1:36. doi: 10.12688/openreseurope.13420.2. eCollection 2021.
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. The first step of the data-processing method builds a dataset using web-hosted pre-processed data and open-source software. The second step aggregates the dataset using a specified spatial aggregation method. The spatially aggregated dataset is used as input data to construct sector-coupled energy system models. To demonstrate the application of the data processing process, three power and heat optimisation models of Germany were constructed using the proposed data processing approach. Significant variation in generation, transmission and storage capacity of electricity were observed between the optimisation results of the energy system models. This paper presents a novel data processing approach to construct sector-coupled energy system models with integrated spatial aggregations methods.
数据处理是能源系统建模中的关键步骤，它将来自各种来源的输入数据整理成构建模型所需的格式。多个开源网络托管数据库在欧洲范围内提供预处理后的输入数据。然而，能够使用指定的空间分辨率降低方法构建能源系统模型的已记录开源数据处理工作流程数量仍然有限。数据处理方法的第一步是使用网络托管的预处理数据和开源软件构建数据集。第二步使用指定的空间聚合方法聚合数据集。空间聚合后的数据集用作构建部门耦合能源系统模型的输入数据。为了演示数据处理过程的应用，使用所提出的数据处理方法构建了德国的三个电力和热力优化模型。在能源系统模型的优化结果之间，观察到电力发电、传输和存储容量存在显著差异。本文提出了一种新颖的数据处理方法，用于构建具有综合空间聚合方法的部门耦合能源系统模型。
