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Goals

In recent years, the modeling and analysis of energy and infrastructure networks have become increasingly vital due to the rising complexity, decentralization, and interdependence of modern systems. This workshop brings together researchers, engineers, and practitioners to discuss emerging challenges and state-of-the-art methods in network modelling, with a particular focus on system stability, reliability, and optimization.

Key topics include, but are not limited to:

• Dynamic stability in power grids and integrated energy systems
  Modern energy systems, especially electricity grids, are evolving into highly dynamic and decentralized networks. Ensuring stability under rapidly changing loads, renewable generation, and contingencies is critical. This topic includes:
• Small-signal and large-disturbance stability in power grids
• Transient and frequency stability under fluctuating inputs
• Impact of inverter-based resources (e.g., PV and wind systems)
• Multi-energy system dynamics (electricity–heat–gas coupling)
• Tools for stability analysis: eigenvalue analysis, Lyapunov functions, time-domain simulation
• Probabilistic reliability assessment and resilience modelling
  Network reliability refers to the ability of a system to deliver its function consistently and without failure. This theme explores methods to assess and enhance the robustness and fault-tolerance of critical networks, including
  • Probabilistic reliability modelling
  • Cascading failure analysis in power and transport networks
  • Resilience metrics and stress-testing approaches
  • Cyber-physical risk modelling
  • Component-level reliability vs. system-level emergent behavior
• Optimization techniques for large-scale network control and planning
  Optimization plays a key role in designing and operating complex networked systems efficiently. This topic covers:
  • Optimal power flow (OPF), unit commitment, economic dispatch
  • Grid topology optimization and reconfiguration strategies
  • Distributed and decentralized optimization
  • Real-time control under uncertainty
  • Multi-objective optimization: cost, emissions, resilience trade-offs
• Machine learning and data-driven methods in network behaviour prediction
  As data availability increases, machine learning offers powerful tools to model, predict, and control network behavior. Relevant subtopics include:
  • Graph neural networks (GNNs) for grid modelling
  • Time series forecasting for loads, renewable generation, and failures
  • Anomaly detection and predictive maintenance
  • Reinforcement learning for adaptive control and system planning
  • Hybrid physics-informed and data-driven modelling approaches
• Applications in smart grids, transportation, and communication networks
  This cross-cutting topic looks at practical applications of network modelling in emerging domains:
  • Smart grid management and prosumer integration
  • Integration of EVs, storage, and demand-side flexibility
  • Modelling energy-transport-communication interdependencies
  • Urban infrastructure and mobility network planning
  • Emergency response and post-disaster network reconfiguration

We hope this workshop will foster constructive dialogue, initiate collaborations, and provide valuable insights into the evolving landscape of complex networked systems.

Round table discussion with introductory talks given by leading representatives of Czech energy industry and research, and by invited European experts in this field, will form part of the workshop. Language of the round table discussion will be both Czech and English, translation will be provided.

Main output of the workshop will be up-to-date knowledge transfer from the researches to the users and, conversely, opening new research targets and horizons as required by the power and gas industry.

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Venue

Violino club, Krakovská 5, Prague 1 (November 6)

Czech Statistical Office, Na padesátém 81, Prague 10 (November 7)

Czech Technical University, Faculty of Mechanical Engineering, Karlovo nám. 13, Prague 2 (November 8)