Abstract
Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership presents a critical obstacle to the success of such spatio-temporal models, and incentive mechanisms to foster data-sharing need to be considered. The main contributions are a) a comparative analysis of the forecasting models, advocating for efficient and interpretable spline LASSO regression models, and b) a bidding mechanism within the data/analytics market to ensure fair compensation for data providers and enable both buyers and sellers to express their data price requirements. Furthermore, an incentive mechanism for time series forecasting is proposed, effectively incorporating price constraints and preventing redundant feature allocation. Results show significant accuracy improvements and potential monetary gains for data sellers. For wind power data, an average root mean squared error improvement of over 10% was achieved by comparing forecasts generated by the proposal with locally generated ones.

Abstract
The electrification of the transport sector is a critical element in the transition to a low-emissions economy, driven by the widespread adoption of electric vehicles (EV) and the integration of renewable energy sources (RES). However, managing the increasing demand for EV charging infrastructure while meeting carbon emission reduction targets is a significant challenge for charging station operators. This work introduces a novel carbon-aware dynamic pricing framework for EV charging, formulated as a chance-constrained optimization problem to consider forecast uncertainties in RES generation, load, and grid carbon intensity. The model generates day-ahead dynamic tariffs for EV drivers with a certain elastic behavior while optimizing costs and complying with a carbon emissions budget. Different types of budgets for Scope 2 emissions (indirect emissions of purchased electricity consumed by a company) are conceptualized and demonstrate the advantages of a stochastic approach over deterministic models in managing emissions under forecast uncertainty, improving the reduction rate of emissions per feasible day of optimization by 24 %. Additionally, a surrogate machine learning model is proposed to approximate the outcomes of stochastic optimization, enabling the application of state-of-the-art explainability techniques to enhance understanding and communication of dynamic pricing decisions under forecast uncertainty. It was found that lower tariffs are explained, for instance, by periods of higher renewable energy availability and lower market prices and that the most important feature was the hour of the day.

Abstract
Artificial Intelligence (AI) is transforming every aspect of modern society. It demonstrates a high potential to contribute to more flexible operations of safety-critical network infrastructures under deep transformation to tackle global challenges, such as climate change, energy transition, efficiency, and digital transformation, including increasing infrastructure resilience to natural and human-made hazards. The widespread adoption of AI creates the conditions for a new and inevitable interaction between humans and AI-based decision systems. In such a scenario, creating an ecosystem in which humans and AI interact healthily, where the roles and positions of both actors are well-defined, is a critical challenge for research and industry in the coming years. This perspective article outlines the challenges and requirements for effective human-AI interaction by taking an interdisciplinary point of view that merges computer science, decision-making sciences, psychological constructs, and industrial practices. The work focuses on three emblematic safety-critical scenarios from two different domains: energy (power grids) and mobility (railway networks and air traffic management). © 2025 Elsevier B.V., All rights reserved.

Abstract
The increasing penetration of renewable energy sources in power systems has heightened the importance of grid-forming (GFM) converters, which emulate the dynamic behavior of synchronous machines and are crucial for ensuring stability in converter-dominated grids. However, the complexity of modern grids calls for innovative control mechanisms to unlock the full potential of GFM technology. This work presents a novel automated framework for control design in power systems. Simulated annealing is used to evolve the structural design of control systems represented as graph-based models. The method achieves greater flexibility by using control graphs instead of traditional tree-based representations, supporting complex feedback loop configurations. A simplification process is also included to reduce complexity and improve interpretability, ensuring practical applicability. Validation on a two-generator power system with one GFM converter demonstrates the method's ability to design robust controllers that enhance system stability, achieving better performance metrics, such as smoother frequency responses with significantly reduced frequency deviations compared to benchmark configurations. The improved frequency response arises from differing terminal angle profiles, enabling faster, stronger power responses that quickly arrest frequency deviations during disturbances.

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 dayahead 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.