Marie Münster

With electricity demands in the EU expected to increase by up to 50% towards 2050, and variable renewable energy additions increasing by around 25% per year globally, future grid operations will need to become more efficient to support this rapid growth. Optimal Transmission Switching (OTS) can improve efficiency, reduce costs, and relieve congestion in power systems by actively switching off transmission lines and thereby optimizing the network topology. Despite its potential, practical adoption remains limited due to the computational complexity of the alternating current (AC)-OTS problem, which combines non-linear AC power flow equations with binary switching decisions. Recently, machine learning methods have attracted considerable interest in power systems. This paper introduces a graph neural network (GNN)-based solution approach that does not require solving the full ACOTS problem directly. Using ACOPF solutions generated under sampled load and topology variations, we train two GNNs: a classifier to predict feasibility and a regressor to estimate system cost for candidate topologies. To address the combinatorial burden of switching multiple lines, we employ offline and online line selection, adaptive sampling, and an iterative tree-based search that leverages GNN predictions to generate multiple candidate switching plans. Additionally, we use the GNN's gradient to assess solution stability. The proposed method achieves fast and scalable predictions, enabling us to assess multiple scenarios in a fast manner, and make high-quality switching recommendations, offering a data-driven approach towards practical ACOTS implementation.

Future energy systems increasingly relying on variable renewable sources will require flexibility, yet the role and competition of different flexibility providers remain uncertain. We use a pan-European sector-coupled energy system model to quantify flexibility needs across sectors, regions, and timescales in a Net-Zero energy system. We explore uncertainty spaces for flexibility portfolios using 33 weather years and Monte Carlo simulations. Sector coupling unlocks flexibility opportunities, through electrified heating, electric vehicles and hydrogen production. Daily and weekly flexibility are provided by electric heating, electric vehicles, transmission and batteries, whereas annual flexibility relies on hydropower, electrolyzers, electricity transmission -nearly doubled by 2050- and peak gas. Peak gas capacity remains necessary as a low-capacity-factor backup, transitioning from natural gas to biomethane, with a 212 GW median capacity. Uncertainty in electric vehicles and hydrogen deployment impacts the flexibility mix. Our study highlights the need for diverse, cross-sectoral flexibility portfolios across regions and timescales.

Deep emission reductions in heavy transport sectors, such as long-distance aviation and shipping, require either large-scale substitution toward renewable fuels or continued fossil use compensated by carbon capture and sequestration. While renewable fuels rely on scarce resources including renewable electricity, hydrogen, and biomass, compensation of fossil emissions is constrained by the cost and availability of CO₂ capture and the capacity for transport and sequestration. This study applies the sector-coupled energy system model Balmorel-OptiFlow to identify least-cost strategies for achieving a net-zero energy system in Western Europe by 2050. By endogenously optimising transport fuel supply and carbon management across multiple CO₂ value chains, and applying a one-at-a-time sensitivity analysis, the study identifies conditions under which the fuel mix shifts between renewable and fossil fuels. Biofuel deployment scales directly with biomass availability, whereas electrofuels are deployed only when low-cost electricity and hydrogen, or high fossil fuel prices, make them competitive with fossil pathways compensated by carbon capture and sequestration. Even under high renewable fuel uptake, carbon management remains central to the least-cost system, with 145–402 MtCO₂/yr sequestration across scenarios, driven by persistent fossil energy use in aviation and industrial heat.

Reducing emissions from shipping is a global climate challenge, and guiding policy frameworks are currently under negotiation. Decarbonising the sector entails large, irreversible investments that interact directly with the broader energy system. Long vessel lifetimes and constrained fuel supply chains risk locking in decisions made today, creating an urgent need for coordinated planning across sectors. In this study, we develop a spatially detailed energy system model integrating shipping fleet turnover, port‑level fuel demand and infrastructure investment to capture interactions between shipping and the energy system. We show that economy‑wide net‑zero targets do not guarantee fossil‑free shipping, as mitigation can shift to other sectors. Renewable fuels replace fossil fuels only under shipping‑specific net‑zero constraints and without onboard carbon capture, and their emergence systematically reshapes the build‑out of electricity and hydrogen infrastructure. Our results show that coordination across shipping, fuel supply and energy infrastructure is required for a sustainable maritime transition.