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

为了实现气候中立，大规模整合可再生能源和其他能源部门，如气候变化和运输部门的电气化是关键。对于可再生能源的大规模集成，需要一个强大的输电网。然而，输电网的规划过程存在着多尺度的不确定性。因此，预测可能出现的气候趋势或极端天气事件的影响对于规划输电电网至关重要。除了多尺度不确定性外，准确评估终端用户部门电气化的潜在影响是实现具有成本效益的电力系统转型的关键。因此，本项目的目标是在考虑来自扇区耦合的灵活用户的多尺度不确定性的背景下，为稳健的输电网规划开发新的方法。首先，将开发一种方法来模拟电力系统长期规划中的多个时间尺度的不确定性，特别关注天气引起的不确定性。其次，提出了一种基于机组组合模型的综合电网规划模型。在随机调度模型中，不仅确定了常规发电厂和仓库的运行，而且还优化了运输和气候适应部门的部门耦合技术的行为。由于多尺度不确定性的考虑导致问题的高度复杂性，因此需要一种合适的求解方法。因此，在第三步，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点来实现更高的计算性能，并使规划实际规模的电力系统成为可能。因此，拟议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的输电网提供最佳发展路径，并提供证据说明天气引起的不确定性以及运输和气候变化部门对投资决策的影响。

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.