Abstract
The polarization curve characteristics of proton exchange membrane (PEM) hydrogen electrolyzers lead to large variations in the equivalent load impedance over the operating current range. This results in a varying closed-loop system time response when traditional fixed-gain PI controllers are employed. In this work, the design and experimental validation of a 3-phase interleaved buck converter controlled via a proposed adaptive lead-lag current control strategy for a PEM hydrogen electrolyzer load is presented. The incremental load conductance method is used to obtain a control-oriented model of the converter-electrolyzer system, enabling real-time calculation of controller parameters via pole-zero cancellation and user-specified transient performance. A laboratory prototype is implemented to experimentally verify the approach under step-load changes, ramp-load changes, and 50% input voltage sag conditions. The results show less than 1% current ripple, identical transient performance over the entire operating range, and improved disturbance ride-through performance compared to a traditional PI controller. The proposed approach offers a viable and robust control solution for high-current PEM electrolyzer applications.

Abstract
Energy Storage Systems (ESSs) are an important source of flexibility in power systems with high penetration of Renewable Energy Sources (RESs). When installed at transmission-distribution interface nodes, shared ESSs can support both Transmission System Operators (TSOs) and Distribution System Operators (DSOs), but their long-term planning remains challenging because investment decisions depend on coordinated operation under uncertainty and battery degradation over time. This paper proposes a degradation-aware planning framework for shared battery ESSs in coordinated TSO-DSO operation. The problem is formulated as a bi-level stochastic optimization model in which the upper level determines siting, sizing, and staged investment decisions under investment-cost uncertainty, while the lower level evaluates these decisions through coordinated system operation. To preserve tractability, the framework combines Benders' decomposition for long-term planning with an Alternating Direction Method of Multipliers (ADMM)-based decentralized coordination mechanism for short-term operation. The framework is evaluated on integrated IEEE transmission-distribution test systems over a 15-year planning horizon. Relative to uncoordinated operation, coordinated operation with shared ESSs reduces operating costs by up to 18.25% and RES curtailment by up to 92.16% in the later years of the planning horizon, while eliminating voltage violations. The results also show that degradation materially affects ESS valuation and that temporal discretization can influence siting and sizing decisions.

Abstract
The rapid expansion of offshore wind farms and the development of energy islands for green hydrogen production have introduced futuristic off-grid systems. These systems can experience total shutdowns, necessitating black start solutions to ensure reliable restoration capabilities for isolated offshore wind farms. This paper investigates a grid-forming converter sizing strategy to enable black start capabilities in off-grid offshore wind farms. The study evaluates the impact of different energization strategies on battery energy storage system (BESS) sizing, focusing on soft energization with droop control in wind turbines and electrolyzers, the effects of wind turbine ramp rates on BESS requirements, and the role of switchable shunt reactors at the offshore substation for reactive power management. A comparative analysis is conducted between soft + hard and pure soft energization sequences to assess their impact on BESS converter sizing. Results demonstrate that the combined soft + hard energization strategy significantly reduces BESS converter size, offering a more cost-effective black start solution compared to pure soft energization.

Abstract
Maintaining frequency stability is one of the biggest challenges facing future power systems, due to the increasing penetration levels of inverter-based renewable resources. This investigation experimentally validates the frequency provision capabilities of a real Polymer Electrolyte Membrane (PEM) hydrogen electrolyser (HE) using a power hardware-in-the-loop (PHIL) setup. The PHIL consists of a custom 3-level interleaved buck converter and a hardware platform for real-time control of the converter and conducting grid simulation, associated with the modelling of the future Iberian Peninsula (IP) and Continental Europe (CE) systems. The investigation had the aim of validating earlier simulation work and testing new responses from the electrolyser when providing different frequency services at different provision volumes. The experimental results corroborate earlier simulation results and capture extra electrolyser dynamics as the double-layer capacitance effect, which was absent in the simulations. Frequency Containment Reserve (FCR) and Fast Frequency Response (FFR) were provided successfully from the HE at different provision percentages, enhancing the nadir and the rate of change of frequency (RoCoF) in the power system when facing a large disturbance compared to conventional support only. The results verify that HE can surely contribute to frequency services, paving the way for future grid support studies beyond simulations.

Abstract
In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global inter-pretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.