Abstract
The electrification of transportation, driven by the widespread adoption of electric vehicles and increased integration of renewable energy, is critical to decarbonizing mobility and society. Demand response strategies, such as dynamic pricing, enable indirect control of charging processes, but their success relies on accurately estimating consumer responses to tariff changes. Observational data can provide insights into consumer behavior, but the presence of confounding variables motivates the use of causal inference techniques for a reliable elasticity estimation. This study proposes a data-driven framework for optimizing day-ahead charging tariffs, leveraging causal discovery and inference algorithms validated on a synthetically generated dataset. A sensitivity analysis explores the impact of data availability on elasticity estimation and the performance of the resulting demand response strategy. The findings highlight the potential of causal machine learning to characterize consumers and, ultimately, the need for regular characterization to improve the efficiency of demand-side management.

Abstract
Increased electrification of various critical infrastructures has been recognized as a key to achieving decarbonization targets worldwide. This creates a need to better understand the risks associated with future power systems and how such risks can be defined, assessed, and mitigated. This paper surveys prior work on power system risk assessment and management and explores the various approaches to risk definition, assessment, and mitigation. As a result, the paper proposes how future grid developments should be assessed in terms of risk causes, what methodology may be used to reduce the risk impacts, and how such approaches can increase grid resilience. While we attempt to generalize and classify various approaches to solving the problem of risk assessment and mitigation, we also provide examples of how specific approaches undertaken by the authors in the past may be expanded in the future to address the design and operation of the future electricity system to manage the risk more effectively. The importance of the metrics for risk assessment and methodology for quantification of risk reduction are illustrated through the examples. The paper ends with recommendations on addressing the risk and resilience of the electricity system in the future resilient implementation while achieving decarbonization goals through massive electrification.

Abstract
A fast response to faults in large-scale photovoltaic power plants (PVPPs), which can occur on hundreds of components like photovoltaic panels and inverters, is fundamental for maximizing energy generation and reliable system operation. This work proposes using a Graph Neural Network (GNN) combined with a digital twin for synthetic fault data scenario generation for fault location in PVPPs. It shows that GNN can adapt to system changes without requiring model retraining, thus offering a scalable solution for the real operating PVPPs, where some parts of the system may be disconnected for maintenance. The results for a real PVPP show the GNN outperforms baseline models, especially in larger topologies, achieving up to twice the accuracy in a fault location task. The GNN's adaptability to topology changes was tested on the simulated reconfigured systems. A decrease in performance was observed, and its value depends on the complexity of the original training topology. It can be mitigated by using several system reconfigurations in the training set.

Abstract
With the growing need for AI-driven solutions in power grid management, this work addresses the challenge of creating realistic synthetic operating scenarios essential for developing, testing, and validating AI-based decision-making systems. It uses spatial-temporal noise functions, predefined patterns, and optimal power flow to model renewable energy and conventional power plant generation, load, and losses. Quantitative and visual key performance indicators are proposed to evaluate the quality of the generated operating scenarios, and the validation highlights the framework's ability to emulate diverse and practical operating scenarios, bridging gaps in AI-driven power system research and real-world applications.

Abstract
The European Union's Artificial Intelligence (AI) Act defines robustness, resilience, and security requirements for high-risk sectors but lacks detailed methodologies for assessment. This paper introduces a novel framework for quantitatively evaluating the robustness and resilience of reinforcement learning agents in congestion management. Using the AI-friendly digital environment Grid2Op, perturbation agents simulate natural and adversarial disruptions by perturbing the input of AI systems without altering the actual state of the environment, enabling the assessment of AI performance under various scenarios. Robustness is measured through stability and reward impact metrics, while resilience quantifies recovery from performance degradation. The results demonstrate the framework's effectiveness in identifying vulnerabilities and improving AI robustness and resilience for critical applications.

Abstract
This paper presents an optimization model for electric vehicle (EV) fleet charging under MIBEL (Iberian Electricity Market). The model integrates EV charging with day-ahead forecasting for grid energy prices, photovoltaic (PV) generation, and local power demand, combined with a battery energy storage system (BESS) to minimize total charging costs, reduce peak demand, and maximize renewable use. Simulations across Baseline, Certainty, and Uncertainty scenarios show that the proposed approach would reduce total charging costs by up to 49%, lower carbon emissions by 73.7%, and improve SOC compliance, while smoothing demand curves to mitigate excessive contracted power charges. The results demonstrate the economic and environmental benefits of predictive and adaptive EV charging strategies, highlighting opportunities for further enhancements through real-time adjustments and vehicle-to-grid (V2G) integration.