The growing integration of converter-interfaced Renewable Energy Sources (RES) has diminished synchronous inertia, complicating short-term frequency stability. Fast Active Power Response (FAPR) from Modular Multilevel Converters (MMCs) in offshore Wind Power Plants (WPPs) is crucial for mitigation. Yet, Transmission System Operators (TSOs) often lack proprietary control details for real-time assessment. This paper proposes an Artificial Neural Network (ANN)-based method to estimate FAPR in mixed systems of MMCs and Synchronous Generators (SGs) using only grid-observable measurements: frequency, Rate of Change of Frequency (RoCoF), and initial loading. A synthetic dataset was generated via RSCAD simulations of a multi-terminal HVDC network, with variations in inertia, loading, and wind speed. The ANN maps frequency/RoCoF polynomials to SG/MMC power response trajectories. Results demonstrate mean absolute errors of approximately 130-160 MW for the reconstructed curves, enabling TSOs to infer FAPR without proprietary control knowledge and enhancing frequency security in converter-dominated grids.

The increasing interdependence of transmission and distribution networks calls for system-wide voltage state estimation. Nevertheless, measurements remain fragmented across operators, and data sharing is often restricted, leaving each entity able to observe and supervise only a subset of system variables. This paper addresses this issue by formulating state estimation as a regression problem with incomplete labels and heterogeneous inputs, using a physics-based integrated transmissiondistribution model as a structured data generator. Steady-state AC power-flow simulations produce diverse operating scenarios with measurement noise and operator-level data partitioning; a residual multilayer perceptron then predicts real and imaginary voltage components using a masked-loss formulation that enables training despite missing target states, with the entire procedure carried out in a federated learning framework so that raw data remain local to each operator. Results demonstrate that the proposed representation and training strategy achieve accurate reconstruction of global voltage states despite fragmented observability and noisy inputs, confirming the feasibility of privacypreserving collaborative state estimation in multi-operator power systems.

Industrial electrification plays a crucial role in reducing carbon dioxide emissions, and ensuring power reliability is important in this process. Reliability and techno-economic evaluations are fundamental to designing, operating, and managing power systems, ensuring that electricity is delivered continuously and securely under various conditions. In particular, maintaining a reliable power supply to industrial loads is critical, especially when renewable sources are present, as these introduce greater variability and uncertainty into the operation of industrial systems. Therefore, this document aims to use a cost-effective storage approach to ensure the reliable operation of sustainable industrial multi-energy systems. In addition, three storage mitigation strategies against random operation are formulated based on financial, technical, practical, and other aspects. A synthetic industrial model consisting of generic component representations in DIgSILENT PowerFactory 2024 is taken as a case study. The structure and parameters of the synthetic model are inspired by data from the literature and a hypothetical projection of a future evolution of a 500 MW sustainable industrial multi-energy system in Rotterdam by 2035. Numerical results provide insight into the flexible and cost-effective operation of sustainable industrial multi-energy systems within the context of decarbonised future Dutch energy systems.

The exponential increase in the integration of Variable Renewable Energy Sources and responsive storage, compensation, and prosumers in electrical power systems raises many uncertainties that affect the operation, control, and planning across different time horizons. Dynamic stability refers to a system's ability to withstand and recover from disturbances while ensuring that systemic symptoms (e.g., voltages, currents, frequency, angular displacements) remain within acceptable limits under both normal and abnormal conditions. Unacceptable excursions in systemic symptoms can cause disruptions or blackouts. A suitably developed and calibrated digital model for dynamic simulations is a key tool for this purpose. This manuscript overviews the development of a digital synthetic model for in-depth analysis and identification of the occurrence and propagation of potential instability issues. The synthetic model is inspired by accessible data on the hypothetical future situation (e.g., year 2030) of the Dutch Power System. The model has been built on the basis of generic component models and parameters from the literature, and several disturbances are evaluated by time-domain simulations. Renewable power electronic-interfaced generators and remaining synchronous generators have implemented emerging methods to provide primary control for active and reactive power support in line with the state-of-the-art recommended practice. This model is proposed as the basis for studying different stability phenomena and challenges for controller design in future operating conditions of the Dutch system in light of the large-scale addition of renewable generation.

As electrical systems become increasingly complex with the integration of new electronic loads and variable renewable energy sources (VRES), modern tools are essential for their effective management and operation. This paper discusses an initial step toward the complete implementation of a digital twin for the Dutch electrical power grid: the development of a real-time digital model. This model represents the Randstad region’s electrical grid, which has recently been enhanced by substantial offshore wind power installations, including Hollandse Kust Zuid and Hollandse Kust Noord. The Real-Time Electromagnetic Transient (EMT) model described in this study enables the assessment of the impacts of offshore wind integration on network stability and power quality. Network elements have been modeled using RSCAD and implemented within the Real-Time Digital Simulator (RTDS). Detailed simulations are conducted to evaluate the grid’s capacity to handle the active and reactive power influx from the offshore wind farms. This study highlights the critical role of precise modeling in ensuring the reliability and efficiency of wind power integration into the national grid.

The integration of variable renewable energy sources (VRES) into the Dutch transmission network is imperative for transitioning to a sustainable energy future. However, incorporating large-scale VRES poses significant monitoring and control challenges, such as fluctuating power flows and grid stability concerns. This manuscript examines the role of digital twins as modern tools for supervising and controlling power systems. This work also presents the development of a synthetic digital model of the Dutch extra-high-voltage (EHV) network to analyze steady-state performance under high VRES penetration scenarios for 2030. Using DIgSILENT PowerFactory, automated by Python scripting, this study offers insights into the impacts of VRES and electrolyzers in power networks. By creating and analyzing various future scenarios, this research evaluates the effectiveness of digital models in scenario analysis, marking a significant step toward the implementation of comprehensive digital twins for future energy system planning and optimization.