Abstract
This paper provides an overview of the application potential of artificial intelligence (AI) in power systems and points towards prospective developments in the fields of AI that are promised to play a transformative role in the evolution of power systems. Among the basic requirements, also imposed by regulation in some places, are trustworthiness and interpretability. Large language models, foundation models, as well as neuro-symbolic and compound AI models, appear to be the most promising emerging AI paradigms. Finally, the trajectories along which the future of AI in power systems might evolve are discussed, and conclusions are drawn.

Abstract
This chapter explores the potential of thermal energy storage (TES) systems towards the decarbonization of industry and energy networks, considering its coordinated management with electrochemical energy storage and renewable energy sources (RES). It covers various TES technologies, including sensible heat storage (SHS), latent heat storage (LHS), and thermochemical energy storage (TCS), each offering unique benefits and facing specific challenges. The integration of TES into industrial parks is highlighted, showing how these systems can optimize energy manage-ment and reduce reliance on external sources. A district heating use case also demonstrates the economic and environmental advantages of a multi-energy management strategy over single-energy approaches. Overall, TES technologies are presented as a promising pathway to greater energy effi-ciency and sustainability in industrial processes.

Abstract
Electric vehicles (EVs) are expected to accelerate the decarbonization of transport while also becoming a highly distributed and flexible resource for power systems. By coupling substantial battery storage with long parking times, EVs can support higher shares of renewable generation through controlled charging and, where available, bidirectional operation (e.g., V1G/V2G and related concepts). At the same time, large-scale EV uptake can increase peak demand, aggravate congestion and losses, and trigger voltage issues (particularly if charging remains unmanaged) potentially leading to costly network reinforcements. This chapter reviews the main EV types, charging modes and technologies (including fast and emerging wireless solutions), and the underlying storage technologies. It then discusses grid-integration architectures and operational strategies, from uncontrolled charging and time-of-use incentives to coordinated “smart charging” and V2G, highlighting their impacts on distribution networks and the requirements for communication, aggregation and system operator interaction. Finally, it outlines a future vision where EV flexibility is integrated with other distributed energy resources to provide local voltage support (active and reactive power), congestion management and frequency regulation services, enabled by appropriate standards, market mechanisms, and regulatory frameworks. © Springer Nature Switzerland AG 2026.

Abstract
Blending green hydrogen into gas networks is subject to strict quality regulation. As Europe diversifies its Liquefied Natural Gas supply, existing literature often ignores the extreme chemical variability of these sources by assuming a static composition. This study proposes a nonlinear steady-state optimization model, strictly adhering to European standards, to maximize hydrogen injection across eight real-world LNG profiles. Results reveal severe sensitivity to the carrier gas: lean gas (Trinidad and Tobago, USA) restricts maximum hydrogen integration to 8.89% and 11.40% by volume due to insufficient heavy hydrocarbons. Conversely, carbon-rich gas (Nigeria) allows up to 20.10%, although inert gases degrade this capacity. Ultimately, this 20.10% maximum blend yields only a 6.77% emission reduction. The imported chemical profile dictates absolute limits, proving that static universal hydrogen quotas are thermodynamically unachievable year-round without continuous dynamic quality tracking.

Abstract
In the coming years, seaports will undergo significant electrification process, moving away from fossil fuels. In such new reality, obtaining accurate electricity load forecasting is critical for reducing costs, planning infrastructure improvements, and ensuring a stable energy supply. However, studies specifically addressing this need in ports are scarce. This paper presents several novel Long Short-Term memory (LSTM) models for forecasting the electricity demand of a highly electrified port, using the Port of Sines as a case study. These models incorporate operational data, such as vessel arrival schedules and quay crane usage, to enhance forecasting accuracy. Our results show that including these variables significantly improves forecast accuracy, reducing the Mean Absolute Percentage Error (MAPE) from 10.55% to 3.59% compared to models relying solely on historical data. This research provides a robust framework for ports to improve energy management and supports the broader goals of energy efficiency and sustainability in the maritime industry. © The Author(s) 2026.

Abstract
Switched Reluctance Motors (SRMs) are becoming increasingly popular for various applications, including automotive applications. However, challenges such as torque ripple and vibration persist, limiting their performance. This chapter investigates the application of intelligent control strategies, particularly fuzzy logic, to mitigate these issues. Fuzzy logic modeling does not require an accurate mathematical model which is very difficult to obtain from a SRM because of its inherit nonlinearities. In this work a Fuzzy Logic Controller (FLC) applied to the speed control of an SRM, highlighting the advantages of FL over traditional methods in terms of flexibility and performance. A comparison is made between the FLC, a Sliding Mode Control (SMC), and a Proportional Integral (PI) controller. Simulation results using MATLAB/Simulink show that the FLC substantially reduces torque ripple, offering better overall performance in terms of smoothness and robustness under varying operational conditions. The findings demonstrate that FLC offers a more effective solution than conventional approaches for SRM applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.