Plenary sessions
Methods and tools to assess the transient stability of future power grids
| by Pr. Xavier Kestelyn ,
(Click To read the biography) Lille, France, |
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The ongoing transformation of electric power systems, driven by the large-scale integration of converter-interfaced renewable generation, HVDC and multi-terminal DC links, distributed generation, energy storage, and sector-coupling technologies, is profoundly reshaping the dynamic behavior of power grids. Traditional transient stability assessment techniques were primarily developed for power systems dominated by synchronous machines and are increasingly challenged by the characteristics of future converter-rich grids, where reduced inertia, complex control interactions, and multi-timescale dynamics play a central role.
This presentation provides an overview of the principal methods and computational tools available for assessing transient stability in future power systems. Starting from time-domain simulation approaches, the talk discusses the applicability of the equal-area criterion, transient energy functions, and direct methods to low-inertia and highly converter-dominated grids. Particular attention is devoted to advanced modeling paradigms capable of representing hybrid AC/DC systems, grid-forming and grid-following converters, current-limited fault ride-through capabilities, distributed energy resources, and active network control strategies.
The plenary further reviews emerging approaches based on nonlinear dynamical systems theory, Lyapunov methods, model-order reduction and machine learning-assisted stability assessment. These techniques are compared in terms of accuracy, computational efficiency, scalability, and suitability for both real-time operation and long-term planning studies. The role of digital twins, high-performance computing, and data-driven surrogate models in enabling faster and more reliable stability analyses is also examined.
Through representative case studies involving AC/DC benchmark networks and converter-dominated power systems, the talk illustrates how the combination of physics-based models and data-driven surrogates can support system operators, planners, and researchers in addressing the challenges of the energy transition. The presentation concludes with an outlook on future research directions toward trustworthy, explainable, and computationally efficient transient stability assessment frameworks for the next generation of resilient and sustainable power grids.
Keywords: Transient Stability, Converter-Dominated Power Systems, Low-Inertia Grids, Grid-Following and Grid-Forming Control, Lyapunov Methods, EMT and Real-Time Simulation, Machine Learning, Digital Twins, Energy Transition, Power System Dynamics.
DC Grids - A new era in Power Systems
| by Pr. Hugo Morais ,
(Click To read the biography) Lisboa, Portugal, |
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The transition towards a carbon-neutral energy system is transforming the way electricity is generated, distributed, and consumed, creating new opportunities for DC grids as a key element of future power systems. This keynote will explore how DC architectures can improve energy efficiency, facilitate the integration of renewable energy sources and storage systems, and enable new applications such as electric mobility, smart buildings, and industrial energy systems. The presentation will highlight the main results of the Shift 2 DC project, including the development and validation of DC solutions, advanced control and management strategies, interoperability approaches, and real-world demonstrations showing the potential of DC grids to reduce conversion losses, increase system flexibility, and support the transition towards more sustainable and resilient energy infrastructur
Artificial Intelligence and the Future of Industrial Systems
| by Pr. Tiago Campelos Pinto ,
(Click To read the biography) UTAD - INESCTEC, Portugal, |
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Modern industrial ecosystems are undergoing a profound digital and ecological transition, marked by increasing operational complexity, strict sustainability imperatives, and the demand for autonomous, resilient operations. While classical control theory provides deterministic guarantees of stability, robustness, and safety, modern physical processes increasingly operate under dynamic, uncertain, and highly coupled conditions that challenge conventional model-based frameworks. This keynote addresses the synergistic convergence of Artificial Intelligence (AI) and Control Engineering, charting the evolution of industrial systems toward intelligent, self-optimizing cyber-physical architectures. The talk explores how data-driven modeling, physics-informed machine learning, and multi-agent coordination enhance real-time decision-making, predictive maintenance, and dynamic optimization across complex infrastructure. Emphasizing the deep interdependence among industrial production, electrified mobility, and smart energy grids, we discuss the transition from centralized automation to distributed, collaborative intelligence capable of balancing energy efficiency with operational throughput. Crucially, the presentation addresses foundational challenges in deploying AI within safety-critical physical environments: maintaining formal stability margins, ensuring real-time computational feasibility, and integrating explainable AI (xAI) to foster trust and interpretability. Bridging rigorous control-theoretic guarantees with adaptive learning algorithms, this keynote outlines a roadmap for cross-disciplinary methodologies that turn intelligent industrial autonomy from theoretical promise into scalable, real-world deployment.
Keywords:
Industrial Cyber-Physical Systems, Physics-Informed Machine Learning, Advanced Control Architectures, Autonomous Systems, Energy Optimization, Multi-Agent Systems.
