论文标题

Pypsa-Earth。非洲展示的新的全球开放能源系统优化模型

PyPSA-Earth. A New Global Open Energy System Optimization Model Demonstrated in Africa

论文作者

Parzen, Maximilian, Abdel-Khalek, Hazem, Fedorova, Ekaterina, Mahmood, Matin, Frysztacki, Martha Maria, Hampp, Johannes, Franken, Lukas, Schumm, Leon, Neumann, Fabian, Poli, Davide, Kiprakis, Aristides, Fioriti, Davide

论文摘要

决策者使用宏观能源系统建模将全球能源过渡转移到负担得起,可持续和可靠的未来。封闭式模型是大多数政策和行业决策的当前标准。但是,事实证明,开放模型是促进科学,强大的技术分析,协作和透明政策决策的竞争替代方案。然而,两个问题降低了采用:开放模型通常以有限的地理范围设计,阻碍协作的协同作用,或者基于空间较低的数据,从而限制了它们的使用。在这里,我们介绍了Pypsa-Earth,这是第一个具有高空间和时间分辨率数据的开源全球能源系统模型。它通过提供可以对世界能源系统或任何子集建模的工具来实现大规模协作。这项工作来自使用新数据和功能的欧洲PYPSA-EUR模型。它适用于运营以及合并的生成,存储和传输扩展研究。该模型提供了两个主要功能:(1)具有全局覆盖范围的可自定义数据提取和准备脚本,以及(2)PYPSA能量建模框架集成。数据包括来自开源的电力需求,生成和中型至高压网络,但可以进一步集成其他数据。广泛的聚类和网格网格划分策略有助于使模型适应计算和实际需求。对整个非洲大陆进行了数据验证,并通过针对尼日利亚的2060净零计划研究对优化功能进行了测试。演示表明,提出的发展可以为能源计划研究建立高度详细的能源系统模型,以支持政策和技术决策。我们欢迎联合起来解决能源过渡的挑战。

Macro-energy system modelling is used by decision-makers to steer the global energy transition toward an affordable, sustainable and reliable future. Closed-source models are the current standard for most policy and industry decisions. However, open models have proven to be competitive alternatives that promote science, robust technical analysis, collaboration and transparent policy decision-making. Yet, two issues slow the adoption: open models are often designed with limited geographic scope, hindering synergies from collaboration, or are based on low spatially resolved data, limiting their use. Here we introduce PyPSA-Earth, the first open-source global energy system model with data in high spatial and temporal resolution. It enables large-scale collaboration by providing a tool that can model the world energy system or any subset of it. This work is derived from the European PyPSA-Eur model using new data and functions. It is suitable for operational as well as combined generation, storage and transmission expansion studies. The model provides two main features: (1) customizable data extraction and preparation scripts with global coverage and (2) a PyPSA energy modelling framework integration. The data includes electricity demand, generation and medium to high-voltage networks from open sources, yet additional data can be further integrated. A broad range of clustering and grid meshing strategies help adapt the model to computational and practical needs. A data validation for the entire African continent is performed and the optimization features are tested with a 2060 net-zero planning study for Nigeria. The demonstration shows that the presented developments can build a highly detailed energy system model for energy planning studies to support policy and technical decision-making. We welcome joining forces to address the challenges of the energy transition together.

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