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.

Integrating gigawatt-scale offshore wind-hydrogen energy systems (OWHESs) is pivotal for the energy transition, yet their dynamic interactions and grid-support capabilities remain insufficiently explored. This paper addresses this gap by developing a real-time electromagnetic transient model of a 2 GW OWHES, which is implemented on a commercial real-time digital simulator (RTDS). Furthermore, a novel communication-free coordinated frequency control strategy is proposed, which synergistically harnesses the flexibility of the HVDC system, wind power plants, and electrolyzer plants. Real-time simulation results demonstrate the model's ability to capture the OWHES dynamics. Moreover, results from a significant generation loss scenario demonstrate the proposed control's superiority over existing methods, as it markedly improves the onshore frequency nadir and reduces the rate of change of frequency. This confirms its effectiveness in enhancing onshore frequency stability and showcases the potential of OWHESs as a valuable source of grid ancillary services.

Multi-energy systems, as emerging power system architectures integrated by multiple converter-controlled wind power plants and electrolyzer facilities, face new stability challenges, among which subsynchronous oscillations (SSOs) have emerged as a critical concern. Despite extensive research on SSO analysis, achieving reliable real-time identification remains challenging due to the nonlinear, time-varying nature, and the limitations of conventional signal-processing techniques. To address this challenge, this paper proposes a data-driven framework based on a one-dimensional convolutional and long shortterm memory neural network (1D-ConvLSTM) for the early identification of low-damping SSO in multi-energy systems. A stochastic database is generated through time-domain simulations covering a wide range of operating conditions and contingency scenarios. Oscillatory parameters are estimated using Matrix Pencil method to systematically label critical SSO events based on damping criteria. The proposed 1D-ConvLSTM model is trained using multivariate time-series measurements to enable early SSO identification. Simulation results obtained from a realistic offshore multi-energy system demonstrate that the proposed approach achieves competitive performance in identifying critical SSO events, outperforming standalone Conv1D and LSTM models. These results highlight the strong potential of datadriven artificial intelligence methods for real-time and predictive identification of SSO in multi-energy systems.

Deep electrification of energy-intensive industry increases exposure to volatile electricity prices and, in the Dutch context, disproportionately high grid tariffs. This paper quantifies the techno-economic value of multi-purpose demand response (DR) for a theoretical 500 MW zero-emission oil refinery in Pernis, the Netherlands, modeled as a coupled electricity-hydrogen multi-energy system. A linear programming model cooptimizes hourly dispatch and capacity sizing of battery storage, hydrogen storage, and electrolyzers, minimizing opportunity, operational, and annualized investment costs over a full simulation year. Results show that DR reduces total annual costs by 24 - 27% relative to the no-flexibility baseline. Grid connection capacity (GCC) emerges as the dominant cost lever: reducing GCC from 500 MW to 204 MW yields an additional instant saving of 73 M€/yr, making optimized DR the most cost-effective zero-emission design.

Grid-integrated electrolyzer systems are increasingly deployed for green hydrogen production, which is a promising pathway for energy decarbonization. However, their operation is challenged by insufficiently understood dynamic interactions among the grid-side rectifier, the buck converter, their control loops, and the electrolyzer stack. To address this issue, this paper develops a holistic analytical framework for such systems. A unified model is derived by integrating the rectifier, the buck converter, their control loops, and the electrolyzer stack. Based on this model, eigenvalue, participation-factor, and frequency-response analyses are conducted to systematically quantify stability characteristics, internal dynamic couplings, and parameter sensitivities. For a 2 MW electrolyzer case, the results reveal that excessive rectifier or buck-control bandwidths can independently trigger distinct oscillatory instabilities. On this basis, engineering-oriented controller-tuning guidelines are established, recommending about 10–50 Hz for the phase-locked loop, below about 60 Hz for the DC-link voltage controller, and about 20–150 Hz for the buck power controller. The analysis further shows that properly designed buck bandwidth renders the system-level power response weakly sensitive to slow electrolyzer dynamics dominated by double-layer capacitance, thereby mitigating uncertainty in this capacitance and clarifying the applicability of reduced-order electrolyzer models. These findings are corroborated by PSCAD/EMTDC time-domain simulations, verifying the effectiveness of the proposed analytical model. Additional verifications under frequency and voltage disturbances further confirm the model’s predictive capability, with maximum relative errors of 0.264%–1.486% and 0.153%–5.922%, respectively. Overall, this work offers an efficient analytical tool for stability-oriented control design, model-fidelity selection, and dynamic interaction analysis of grid-integrated electrolyzer systems.

The increasing integration of inverter-based resources (IBRs) in power systems raises questions regarding the applicability of short-circuit calculation methods originally developed for synchronous generation. This work evaluates the limitations of IEC 60909 in systems with high photovoltaic penetration by comparing its results with dynamic RMS and EMT simulations, used as reference, and with two alternative approaches: The Complete Method and a Fault-Ride-Through-based method. The analysis is performed on the IEEE 14-bus system considering 25% and 50% IBR penetration levels and three fault types: Three-phase, lineto-line, and single line-To-ground faults. The results show that IEC 60909 systematically overestimates short-circuit currents, with errors strongly dependent on the fault type, reaching up to 76% in single line-To-ground faults at 50% IBR penetration. The alternative methods significantly improve accuracy, but on different fault types. The FRT-based method provides the best performance for three-phase and line-To-line faults, while the Complete Method achieves higher accuracy in single line-To-ground faults. These findings suggest that no static method is uniformly suitable across all fault types, and that a complementary approach is required for short-circuit analysis in inverter-dominated systems.

This paper proposes an integrated planning-operation framework coupling data-driven wind modeling with bi-layer optimization for hydrogen-integrated power systems. To address uncertainty, a stacked Long Short-Term Memory (LSTM) network is used to generate year-long hourly offshore wind profiles, preserving multiscale temporal dynamics. These profiles are embedded into a bi-layer optimization structure: an upper-layer Particle Swarm Optimization (PSO) determines the siting and sizing of hydrogen-to-power assets, while a lower-layer Mixed-Integer Linear Programming (MILP) model ensures operational feasibility under network, storage, and capacity constraints. The framework is validated on a modified IEEE RTS-24 system with large-scale offshore wind. Results demonstrate that coordinated fuel cell and hydrogen-fired gas turbine deployment significantly improves performance, reducing wind curtailment by 48.5% and load shedding by 96.6%, while cutting annual costs by $ 20.06 M. Despite hydrogen's modest generation share, it serves as a critical flexibility and reliability enabler. Ultimately, this study demonstrates how neural-network-based uncertainty modeling effectively informs strategic, system-level investment decisions in modern power grids.

Power systems with increasing integration of power electronic converters are characterized by low inertia, limited short-circuit support, and fast dynamic behavior, making them vulnerable to active power imbalances. Under such conditions, frequency excursions and rapid change in rates of change of frequency (RoCoF) can threaten system stability. Although power electronic interfaced (PEI) units can provide fast frequency support (FFS), their uncoordinated operation may lead to inefficient or even adverse control actions. This paper proposes a coordinated optimization framework for the FFS in multi-area-energy systems incorporating MMC-based HVDC links, electrolyzers, and wind turbines. The framework optimally tunes the control actions of all participating resources to minimize frequency deviations across interconnected areas, while enabling effective sharing of active power imbalances through HVDC links. The integration of wind turbines as additional fast-acting resources is explicitly investigated, highlighting their role in improving the initial frequency response. The proposed approach is validated on a modified multi-energy HVDC system using RMS simulations in DIgSILENT PowerFactory, with the optimization problem solved by the mean-variance mapping optimization (MVMO) algorithm. Simulation results demonstrate that coordinated utilization of HVDC links, electrolyzers, and wind turbines significantly enhances frequency performance, improving RoCoF, reducing frequency nadir, and achieving better steady-state recovery under severe disturbance scenarios.

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 expansion of offshore wind power plants (OWPP) necessitates power system studies of wind power plant due to large number of interfaced converters in wind turbine. The traditional detailed model of wind farms although provides high fidelity but leads to computational complexity and long simulation time. Due to this, simplified equivalent modeling approaches such as the single-machine (SM) equivalent model are commonly adopted in the system studies. This approach considers similar wind incident on all turbines and ignores the losses due to collector cables which is unsuitable due to the complexity and scaling of the network. However, the suitability of such equivalent representation for large OWPPs under multiple-event scenarios remains insufficiently examined in the existing literature. Therefore, this paper investigates the dynamic suitability of a single-machine and multi-machine equivalent model for a 400 MW HVDC-connected OWPP in DIgSILENT PowerFactory. Both aggregated models are examined against each other and the detailed WPP under sequential event scenarios. The comparison is performed based on the RMS dynamic response as well as quantitative performance metrics such as root mean square error (RMSE) and fit percentage. The results indicate that the SM equivalent model only captures the dominant dynamic behavior and reduces the computation time by 84%, whereas the multi-machine model provides an improved accuracy for transient response with a reduction of computation time by 77%.

Coordination between transmission system operators (TSOs) and distribution system operators (DSOs) can support TSOs in using the distribution system (DS) flexibility while ensuring feasible operation. Flexibility areas (FAs) can support TSO-DSO coordination, aggregating the total feasible flexibility within the DS. However, existing real-time estimation approaches do not consider the limited measurements within DS. This paper proposes a Bayesian neural network (BNN) to estimate the operating conditions that bound the operational flexibility, including epistemic and aleatoric uncertainties. These uncertainties stem from the limited real-time measurements in DSs and the measurement noise. TSOs can select a threshold that confirms a probability of safety, considering uncertainty margins. The paper also provides FA estimation in DS topologies with (Formula presented.) points of common coupling (PCC) with the transmission system. Case studies in the CIGRE and Oberrhein networks compare the proposed BNNs to baseline statistic-based approaches for forecast and measurement uncertainty in FAs. The case studies show the proposed FA estimation under various safety margins and systems with 2-PCC. Case studies also assess various measurement noise levels and evaluate model performance for different DS topologies.

This paper proposes a non-linear DC power modulation strategy for expandable point-to-point (PtP) high-voltage DC (HVDC) systems. The goal is to enhance the active power management during post-fault conditions of the interconnected AC networks. The proposed strategy is developed by defining exponentially decaying functions, which, depending on the HVDC network configuration of the expandable HVDC system, alter the active current reference in a voltage source converter (VSC) affected by an AC network's disturbance, without utilizing proportional-integral (PI) controllers. Furthermore, it is investigated whether the produced alteration can fulfill the post-fault active power recovery (PFAPR) requirements of VSC-HVDC systems, even in situations when no communication protocols between the VSC units are used. Lastly, it is demonstrated, through simulation experiments, that the expandable HVDC system (working in a point-to-point (PtP) or a multi-terminal (MT) network configuration), shows a better performance (in terms of the PFAPR profile and the DC voltage response) when the proposed strategy is utilized instead of conventional main-supplementary or droop control strategies.

Achieving carbon neutrality in industrial ports demands a radical transformation of current energy systems. This paper presents a model-based optimization approach for the operation of a multi-energy cluster, considering a hypothetical evolution of a multi-energy industrial cluster in the Netherlands. The aim is to establish a new system operation strategy that supports the transition towards a carbon-neutral energy system. The synthetic model of the used multi-energy cluster integrates five energy carriers - electricity, natural gas, hydrogen, ammonia, and heat - using an energy hub approach to enable sector coupling and enhance flexibility. Physics-based modeling of electrical power flows is included to ensure technical feasibility in the power system. The model minimizes total cluster's cost while ensuring reliable energy supply. The optimization is implemented in Python by using the PyPSA toolbox and mixed-integer linear programming. A full-year, hourly-resolution simulation under three weather scenarios reveals optimal system operation strategies. Numerical results highlight the benefits of multi-energy cluster operation for managing renewable variability and identify ammonia as a key flexibility provider, supporting hydrogen and electricity systems through conversion and storage. The strategy emphasizes cross-sector economic optimization, dynamic dispatch, and enhanced flexibility, offering practical insights for decarbonizing industrial ports and informing future energy investment planning.

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.