Abstract
The deployment of a large number of Distributed Energy Resources (DERs) at the Distribution Network (DN) level brings a much needed level of demand-side flexibility that power systems characterized by a large integration level of Renewable Energy Sources (RESs) require, and will increasingly require in the future. However, until now, the potential of this growing flexibility is under-exploited, as it is not shared with the Transmission Network (TN) level. To harness this valuable flexibility for the benefit of the overall electric power system, efficient and effective coordination mechanisms must be established. This paper compares the two main categories of coordination approaches between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) proposed in the literature, hierarchical and distributed mechanisms. The comparison focuses on the computational effort, operational cost, and RES integration level, highlighting the respective advantages and drawbacks of each coordination model.

Abstract
The HyStorization project aims to advance the modelling and operational understanding of hybrid electrochemical energy storage systems, focusing on Lithium-ion (Li-ion) and Vanadium Redox Flow Batteries (VRFBs). These technologies are key enablers of flexible, reliable, and scalable grid-scale energy storage. While Li-ion batteries are well-established for high-power applications, VRFBs offer promising advantages for medium- to long-duration storage due to their durability and decoupled energy and power capacities. The primary objective is to develop linearized battery models for both technologies, derived from experimental data, that accurately capture efficiency and power limits as functions of the State of Charge (SoC). These models are intended for integration into Mixed-Integer Linear Programming (MILP) tools to optimize energy dispatch in hybrid storage systems. A comprehensive testing campaign was conducted on three BYD stationary Li-ion battery systems. Due to a malfunction in one unit, the remaining three—of similar age and usage—were treated as a single representative system. A Python-based controller was developed to automate cycling and collect high-resolution data (1-second intervals) via HTTP. The testing protocol included: • Constant power cycles for initial validation and degradation screening. • Constant current cycles for parameter extraction. Key findings include: • A slight but consistent improvement in SoC estimation accuracy using a linear model over a bucket model (~2% reduction in MAE and MSE). • Shorter resampling intervals (e.g., 1-minute vs. 15-minute) improved accuracy, but the most significant reduction in error came from refreshing the SoC with real measurements rather than relying on estimated values. • SoC limits, while useful for safety, were found to be overly restrictive and may not reflect the battery’s full operational flexibility. • Attempts to assess cyclic degradation were inconclusive due to the limited number of cycles and short observation window. The final linear model includes parameters for nominal charge/discharge voltages, inverter efficiencies, and dynamic SoC limits as functions of DC power. These were validated against real operational data and compared with manufacturer-based models. Concerning the VRFB, the project originally planned to conduct targeted tests on the VRFB to: • Evaluate energy efficiency across different SoC levels and operational ranges. • Determine maximum and minimum effective power ratings as functions of SoC. • Support the development of non-linear models that will be linearized for MILP integration. However, due to a malfunction, the VRFB could not be tested as planned. Instead, the projectrelied on previously collected characterization data, which did not fully cover the intended test scope. Despite these limitations, the available data was used to: • Analyse energy efficiency trends across selected states of charge (SoC) and operational conditions. • Estimate effective power ratings within the constraints of the existing dataset. • Support the preliminary development of non-linear models, with the aim of future linearization for MILP integration. While these efforts provided valuable insights, the absence of new experimental data limited the ability to fully capture the unique operational characteristics of VRFBs, such as their decoupled energy and power capabilities and their suitability for long-duration storage. The project is expected to deliver: • Validated, MILP-compatible models for both Li-ion and VRFB technologies. • Enhanced dispatch strategies for hybrid storage systems. • Improved integration of real-time SoC measurements to reduce estimation error. • Recommendations for longer-term testing to better assess degradation and refine model accuracy. In conclusion, the HyStorization project provides a foundational step toward more accurate, data-driven modelling of hybrid storage systems. It highlights the importance of real-time data, flexible modelling approaches, and the need for continued testing to support the evolving role of batteries in grid operations.

Abstract
The structural assessment of submerged cathodic protection systems in Offshore Wind Turbines (OWTs) is crucial for ensuring longevity and operational efficiency. Traditional underwater inspections are expensive, inefficient, and expose human divers to hazardous conditions.This article aims to enhance the perception capabilities of underwater vehicles by introducing the Contextual Anode Locator in Varying Underwater Scenarios (CALVUS), a learning-based solution designed for the robust and precise detection of sacrificial anodes in harsh subsea environments. CALVUS leverages the feature extraction capabilities of a depth estimation ViT-based backbone to detect anode structures under challenging underwater conditions such as heavy marine snow, variable illumination, biofouling and motion blur.Evaluation on a dataset composed of images captured at the ATLANTIS Test Centre, CALVUS shows a performance of AP@50 of 97.9 %, an improvement of 19.9 % over state-of-the-art networks such as YOLO and RT-DETR. These results demonstrate the added value of using depth features during the detection operation, ultimately contributing to improved OWT operational efficiency and reduced maintenance costs. © 2025 IEEE.

Abstract
Autonomous landing for Unmanned Aerial Vehicles (UAVs) requires both precision and resilience against environmental uncertainties, capabilities that current approaches struggle to deliver. This paper presents a novel learning-based solution that combines an advanced multimodal transformer-based detector with a reinforcement learning formulation to achieve reliable autonomous landing behavior across varying scenario uncertainties. Beyond the integration of multimodality for robust target detection, this research incorporates a comprehensive analysis of the impact of state representation on decision-making performance. The proposed methodology is validated through extensive simulation studies and real-world field experiments conducted on physical UAV platforms under natural wind disturbances, demonstrating reliable transfer from simulated training environments to controlled outdoor conditions. Field experiments across varying initial conditions and wind stress confirm the system’s robustness, achieving landing precision of 0.10 ± 0.08 meters in outdoor trials, demonstrating centimeter-level accuracy that surpasses the meter-level precision of global positioning systems.

Abstract
Interoperability among diverse devices, from traditional substation control rooms to modern inverters managing components like Distributed Energy Resources (DERs), is a primary challenge in modern power systems. It is essential for streamlining decision-making and control processes through effective communication, ultimately enhancing energy management efficiency. This paper introduces the open-source Legacy Protocol Converter (LPC) grounded in the IEEE 2030.5 standard, which incorporates advanced features for improved adaptability. The LPC bridges legacy equipment using standard protocols such as Message Queuing Telemetry Transport (MQTT) and Modbus with a light-weight asynchronous Neural Autonomic Transport System (NATS) communication system. In light of the limitations inherent in traditional synchronous RESTful systems-specifically those compliant with IEEE 2030.5 that are incapable of facilitating multiple endpoints-the adoption of asynchronous NATS is implemented. This approach can notably enhance communication flexibility and performance. The implementation is containerized for efficient service orchestration and supports the reusability of solutions. The LPC is engineered for seamless integration of DERs with Energy Management System (EMS), aggregation platforms, and Hardware-in-the-loop (HIL) testing environments. In this paper, the LPC has been tested and further developed in various use cases such as multi-physics optimization involving HIL and fast frequency services, e.g., virtual inertia and load shedding, each in a different architectural setup. The findings validate the applicability of LPC not only for devices within modern power systems, but also for heat pumps in the thermal energy sector, facilitating sector coupling. Moreover, the paper provides additional insights into LPC's functionality, reaffirming its efficacy as a scalable, robust, and user-friendly solution for bridging legacy systems through the enhanced IEEE 2030.5 standard designed for the monitoring and control of DERs.

Abstract
Accurately imputing missing data is critical in time series analysis. The present work compares Foundation Model Chronos against Linear Interpolation, K-Nearest Neighbor Imputer, and Gaussian Mixture Model Imputer with three types of missing data patterns: random, short sequential chunks, and a long sequential chunk. These results confirm that for random missing values, KNN and interpolation yield the highest performance, while Chronos outperforms these on sequences. Indeed, however, for longer sequences of missing values, Chronos starts suffering from cascading errors which eventually allow the simpler imputation methods to outrank it. Another test with limited quantities of training data showed different trade-offs for the different methods. Unlike KNN and interpolation, which smooth out the gaps, Chronos generates variable synthetic data. This can be beneficial in tasks which require control or simulation. The results highlight the strengths and weaknesses of the imputers and, therefore, offer practical insights into trade-offs between computational complexities, accuracy, and suitability for time series imputation scenarios.