Electrolytic hydrogen is expected to play a key role in decarbonizing energy systems in Europe. However, such projects face uncertain grid access due to congestion and struggle with financial viability. To address growing grid congestion in the Netherlands, different types of non-firm connection and transport agreements (CTAs) and capacity restriction contracts (CRCs) were introduced, enabling client curtailment in exchange for tariff discounts or per-MW compensation. This paper investigates investment and contracting decisions for electrolyzer projects under these novel contracts using a two-stage risk-aware stochastic optimization model that captures uncertainty in electricity prices and grid access. Results show that risk-averse clients prefer non-firm CTA-85 contracts, which balance tariff discounts with curtailment compensation, while risk-neutral clients favor standard non-firm CTAs. The new contract types can lower the break-even hydrogen price by approximately €0.50/kg compared to firm contracts outside congested areas, and thus improve the business case for electrolytic hydrogen in the Netherlands.

In response to increasing grid congestion in the Netherlands, non-firm connection and transport agreements (CTAs) and capacity restriction contracts (CRCs) have been introduced, allowing consumer curtailment in exchange for grid tariff discounts or per-MW compensations. This study examines the interaction between an electrolyzer project, facing sizing and contracting decisions, and a network operator, responsible for contract activations and determining grid connection capacity, under the new Dutch regulations. The interaction is modeled using two bilevel optimization problems with alternating leader-follower roles. Results highlight a trade-off between CRC income and non-firm CTA tariff discounts, showing that voluntary congestion management by the network operator increases electrolyzer profitability at CRC prices below €10/MW but reduces it at higher prices. Furthermore, the network operator benefits more from reacting to the electrolyzer owner's CTA decisions than from leading the interaction at CRC prices above €10/MW. Ignoring the other party's optimization problem overestimates profits for both the network operator and the electrolyzer owner, emphasizing the importance of coordinated decision-making.

Asgard/NOTT (previously Hi-5) is a European Research Council (ERC)-funded project hosted at KU Leuven and a new visitor instrument for the Very Large Telescope Interferometer (VLTI). Its primary goal is to image the snow line region around young stars using nulling interferometry in the L′-band (3.5 to 4.0) μm, where the contrast between exoplanets and their host stars is advantageous. The breakthrough is the use of a photonic beam combiner, which only recently allowed the required theoretical raw contrast of 10-3 in this spectral range. Nulling interferometry observations of exoplanets also require a high degree of balancing between the four pupils of the VLTI in terms of intensity, phase, and polarization. The injection into the beam combiner and the requirements of nulling interferometry are driving the design of the warm optics and the injection system. The optical design up to the beam combiner is presented. It offers a technical solution to efficiently couple the light from the VLTI into the beam combiner. During the coupling, the objective is to limit throughput losses to 5% of the best expected efficiency for the injection. To achieve this, a list of different loss sources is considered with their respective impact on the injection efficiency. Solutions are also proposed to meet the requirements of beam balancing for intensity, phase, and polarization. The different properties of the design are listed, including the optics used, their alignment and tolerances, and their impact on the instrumental performances in terms of throughput and null depth. The performance evaluation gives an expected throughput loss <6.4% of the best efficiency for the injection and a null depth of 1/42.10-3, mainly from optical path delay errors outside the scope of this work.

The energy transition poses a challenge for the electricity distribution network design as new energy technologies cause increasing and uncertain network loads. Traditional static load models cannot cope with the stochastic nature of this new technology adoption. Furthermore, traditional nonlinear power methods have difficulty evaluating very large networks with millions of cables, because they are computationally expensive. This paper proposes a method which uses copulas for modeling the uncertainty of technology adoption and load profiles, and combines it with a fast linear load flow model. The copulas are able to accurately model the stochastic behavior of solar irradiance, load measurements, and mobility data, converting them into electricity load profiles. The linear load flow model has better scalability and stability compared to traditional load flow models. The models are applied to a case study which uses a real-world dataset consisting of a realistic technology adoption scenario and a low-voltage network with millions of cables, which considers both voltage and current problems. Results show that risk profiles can be generated for all cables in the network, resulting in a valuable map for the district network operator as to where to focus their efforts.