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的电气化，如气候适应和运输部门。为了大规模整合可再生能源，需要一个强大的输电网。然而，输电网规划过程中存在多尺度不确定性。因此，预测可能的气候趋势的影响或极端天气事件的发生对于规划输电网至关重要。除了多尺度的不确定性外，准确评估终端用户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，本项目的目标是开发新的方法，在多尺度的不确定性的背景下，考虑灵活的消费者从部门耦合的强大的输电网规划。首先，在长期的电力系统规划中，在多个时间尺度的建模不确定性的方法，特别关注天气引起的不确定性将被开发。其次，提出了基于机组组合模型的综合输电网规划模型。在随机调度模型中，不仅确定了传统发电厂和存储的操作，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。由于考虑多尺度不确定性导致问题的高度复杂性，因此需要一种非常合适的求解方法。因此，在第三步中，将开发混合分解方法。该方法将基于分布式计算以及列生成和共享算法与最先进的分解方法的结合。它将利用不同分解方法的优势，以实现增强的计算性能，并使规划现实规模的电力系统。因此，拟议的一套方法将提供一个框架，强大的规划大型电力系统。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，并就天气引起的不确定性以及运输和气候适应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.