Robust transmission grid planning under a changing climate: high-resolution models and efficient optimization methods

要实现气候中和，关键是大规模整合可再生能源和其他能源部门（如气候和运输部门）的电气化。对于可再生能源的大规模整合，需要一个强大的输电网。然而，输电网规划过程存在多尺度不确定性。因此，预测可能的气候趋势或极端天气事件的发生对输电网的规划至关重要。除了多尺度的不确定性之外，准确评估最终用途部门电气化的潜在影响是实现经济高效的电力系统转型的关键。因此，该项目的目标是在考虑部门耦合的灵活消费者的多尺度不确定性背景下，为稳健的输电网规划开发新的方法。首先，将开发一种模拟长期电力系统规划中多时间尺度不确定性的方法，特别关注天气引起的不确定性。其次，提出了基于机组承诺模型的输电网综合规划模型。在随机调度模型中，不仅确定了传统电厂和储能系统的运行，而且优化了交通和气候部门的部门耦合技术行为。由于考虑了多尺度不确定性，问题的复杂性很高，需要一种合适的求解方法。因此，在第三步中，将开发一种混合分解方法。该方法将基于分布式计算，并将列生成和共享算法与最先进的分解方法相结合。它将利用不同分解方法的优势来实现增强的计算性能，并使规划现实尺寸的电力系统成为可能。因此，所提出的一套方法将为大规模电力系统的稳健规划提供一个框架。拟议的规划框架将用于为欧洲和智利输电网制定最佳发展路径，并为天气引起的不确定性以及运输和气候部门对投资决策的影响提供证据。

To reach climate neutrality a large-scale integration of renewable energy sources and the electrification of other energy sectors such as the climatization and transport sectors is key. For the large-scale integration of renewable energy sources, a robust transmission grid is required. However, the planning process of transmission grids underlies multi-scale uncertainties. Thus, forecasting impacts of possible climate trends or the occurrence of extreme weather events is crucial for planning transmission grids. In addition to multi-scale uncertainty, accurately valuing the potential impacts of electrification of end-use sectors is key to achieve a cost-efficient power system transformation. Therefore, the objective of this project is to develop novel methodologies for robust transmission grid planning in the context of multi-scale uncertainties considering flexible consumers from sector coupling. Firstly, a methodology for modelling uncertainty in multiple time scales within long-term power system planning, with special focus on weather-induced uncertainty will be developed. Secondly, an integrated transmission grid planning model based on a Unit Commitment model will be proposed. Within the stochastic dispatch model not only the operation of conventional power plants and storages is determined, but also the behaviour of sector-coupling technologies in the transport and climatization sectors is optimized. Due to the high problem complexity resulting from the consideration of multi-scale uncertainties, a well-suited solution method is required. Therefore, in a third step, a hybrid decomposition method will be developed. The method will be based on distributed computing and the combination of the Column Generation & Sharing algorithm with state-of-the-art decomposition methods. It will exploit the strengths of the different decomposition methods for achieving enhanced computational performance and enable planning realistic-size power systems. As such, the proposed set of methodologies will provide a framework for robust planning of large-scale power systems. The proposed planning framework will be used to generate optimal development pathways for the European and Chilean transmission grid, and to generate evidence on the impact of weather-induced uncertainties and the transport and climatization sectors on investment decisions.