TenneT

Energy system models are increasingly dependent on representative climate input. Yet, a fundamental mismatch persists between the hundreds of simulated years often used in climate science and the handful of years that computationally demanding power system models can process. Current practice, including ENTSO-E's European Resource Adequacy Assessment, relies on climate year selections that have not been validated against explicit representativeness criteria. This risks biased investment decisions and blind spots for plausible weather conditions. This study proposes simulated annealing as an optimisation method for selecting representative subsets of complete climate years from large climate ensembles. Representativeness is quantified using the seasonal sliced Wasserstein distance, a metric from optimal transport theory that captures representativeness on marginal distributions, inter-variable correlations, and seasonal structure simultaneously. We evaluate simulated annealing against the alternative methods random search, filtered random search, and K-Medoids clustering across three test cases spanning the Netherlands and Europe, using 180 climate years from the Pan-European Climate Database as a reference. Simulated annealing consistently produces the most representative subsets and outperforms all compared methods. Simulated annealing achieves an effective sample size four to five times the actual subset size. The resulting subsets are roughly 2.5–3.5 times more representative than current ENTSO-E practice. The method is application-agnostic and its output can serve as a validated climate data input to any subsequent (energy) impact study.

Power grid operation is becoming more complex due to the increase in generation of renewable energy. The recent series of Learning To Run a Power Network (L2RPN) competitions have encouraged the use of artificial agents to assist human dispatchers in operating power grids. However, the combinatorial nature of the action space poses a challenge to both conventional optimizers and learned controllers. Action space factorization, which breaks down decision-making into smaller sub-tasks, is one approach to tackle the curse of dimensionality. In this study, we propose a centrally coordinated multi-agent (CCMA) architecture for action space factorization. In this approach, regional agents propose actions and subsequently a coordinating agent selects the final action. We investigate several implementations of the CCMA architecture, and benchmark in different experimental settings against various L2RPN baseline approaches. The CCMA architecture exhibits higher sample efficiency and superior final performance than the baseline approaches. The results suggest high potential of the CCMA approach for further application in higher-dimensional L2RPN as well as real-world power grid settings.

In 2023, Germany's electricity trade balance shifted from net exports to net imports for the first time since 2002, resulting in an increasing discussion of these imports in the public debate. This study discusses different data driven approaches for the analysis of Germany's cross-border trade, with a focus on the methodological challenges to determine the origin of imported electricity within the framework of European electricity market coupling. While scheduled commercial flows from ENTSO-E are often used as indicators, generally these do not correspond to bilateral exchanges between different market actors. In particular, for day-ahead market coupling only net positions have an economically reasonable interpretation, and scheduled commercial exchanges are defined through ex-post algorithmic calculations. Any measure of the origin of electricity imports thus depends on some underlying interpretation and corresponding method, ranging from local flow patterns to correlations in net positions. To illustrate this dependence on methodological choices, we compare different approaches to determine the origin of electricity imports for hourly European power system data for 2024.