The global transition to renewable energy is transforming power systems, necessitating advanced transmission solutions to ensure reliability and resilience. High-voltage direct current (HVdc) systems, particularly multiterminal dc (MTdc) networks, are pivotal in integrating diverse renewable energy sources into hybrid ac/dc networks. These systems facilitate efficient power transfer over long distances and enable dynamic energy sharing across regions. However, the increasing penetration of inverter-based resources introduces complex control challenges that must be addressed to maintain grid stability and resilience.

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 continuous operation and planning of electric power systems undergoes several technical and economic changes associated with environmental and societal goals toward clean, affordable, and resilient sustainable energy supply and deployment. This entails diverse upgrades, which include, for instance, integration with other energy sectors and massive addition of renewable power generation, responsive demand, and different types of storage. The dynamic properties and strength of power systems are evolving toward unprecedented levels with lowered sources for the effective management of active and reactive power balancing in different time scales. Thus, the overall security and reliability performance can be seriously compromised as unexpected disturbances may eventually cause violations to the security limits that are established for the electric power system; this may lead to the outage of important system elements and even partial or total blackouts.

This contribution deals with the optimization of the frequency response of a multi-area, multi-energy HVDC-HVAC cyber-physical power system, representing a power electronic-dominated power system. The system consists of a three-area system, modified so that the areas are electromagnetically decoupled through MMC-based HVDC links, and different controllable energy sources, such as fully decoupled wind turbines type IV and proton exchange membrane electrolysers, are installed at various points of the system. The modified system exhibits three decoupled areas with different generation and demand mixes characterized by different inertia levels and increased controllability due to the converters’ capabilities. The outer controllers of the power electronic interfaced elements installed have been modified with the active power gradient control scheme to respond to frequency excursions and provide fast frequency support to the grid in case of commonly occurred active power-frequency imbalances. A problem formulation for coordinated optimization is presented, aiming at a coordinated tuning of the parameters of the frequency controllers of the synthetic inertia elements participating in the frequency regulation against critical commonly occurred active power-frequency imbalances. The formulations consider the minimization of the dynamic displacements of the areas’ speed following an active power imbalance. To effectively solve the optimization problem and enhance the frequency stability of the system, a powerful metaheuristic optimization algorithm, the mean-variance mapping optimization (MVMO) algorithm, has been utilized. The optimization results can effectively highlight the tuning strategy that achieves the best frequency response of the system under various commonly occurred active power frequency disturbances. It can also provide further insight on the proper utilization of various sources of synthetic inertia with respect to their response capabilities. Finally, the simulation results can also clarify the importance of the location of installation of converter-based elements providing fast frequency support with respect to the grid node the imbalance occurs.

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.

Power-to-hydrogen systems, particularly the most mature alkaline electrolyzers (AELs), are increasingly deployed in modern energy systems due to their pivotal role in green hydrogen production and decarbonization. Proper modeling is vital for optimizing AEL lifecycle decisions, including design, operation, and investment. Despite numerous proposed models, a review focusing on their applications in system-level decision-making (e.g., operation and planning) remains lacking. This paper bridges this gap by reviewing over 100 peer-reviewed articles to offer an in-depth overview of AEL models employed in system-level decision-making. Followed by clarifying modeling requirements across different levels of AEL system analysis, three types of AEL models are classified in system-level decision-making: linear electricity–hydrogen (LEHM), nonlinear electricity–hydrogen (NEHM), and integrated electricity-heat-hydrogen models (IEHHM). This classification is based on representing the AEL with different levels of multi-physics detail and energy conversion assumptions. LEHM assumes a constant electricity-to-hydrogen conversion efficiency of typically about 60%–70%, while NEHM and IEHHM allow modeling of dynamic efficiency variations in the typical range of 60%–80%, where the IEHHM uniquely integrates thermal dynamics. Their modeling principles, characteristics, strengths, and limitations are systematically reviewed, followed by an in-depth overview of their applications and impacts across four applications: economic operation, grid services, heat recovery, and capacity planning. It reveals that LEHM, NEHM, and IEHHM are employed in 35%, 42%, and 23% of these applications, respectively. Finally, a discussion of current modeling limitations and future direction is provided. This paper offers valuable insights and guidance for selecting appropriate AEL models in decision-making studies and identifying pathways for advancing AEL modeling.

As the integration of renewable energy accelerates, ensuring power system stability becomes increasingly critical. This research utilized a Root Mean Square (RMS) synthetic model of the future 380 kV Dutch power system towards 2050 to analyze its oscillatory stability under high renewable penetration and the impact of grid-forming converters under various parametrizations. The presented case study shows that grid-forming (GFM) converters significantly improve frequency stability and damping performance across different perturbations, particularly at higher GFM penetration levels, improving frequency and damping parameters. However, various oscillatory modes present potential stability risks at high penetration levels. The case study also shows minimal differences in controller selection in large-scale models, except under certain conditions. Additionally, the analysis of controller parameters highlighted the critical importance of tuning active power parameters to ensure system stability. The investigation provides essential insights for future power systems, where large-scale integration of renewable energy will necessitate the implementation of converters able to provide ancillary services. The findings emphasize the importance of optimizing GFM converter settings and penetration levels to maintain system resilience, offering valuable guidance for future system planning and regulatory frameworks.

The urgent need for a significant reduction of greenhouse emissions (around 50% by 2030 and net zero by 2050) is driving an acceleration of major technological upgrades in electrical power systems. This is gradually translating into new architectures of transmission and distribution networks. Ideally, the upgraded networks enable the technoeconomic, reliable, and environmentally friendly deployment of a high share of emerging technologies, primarily based on power electronic converters. Such major upgrades occur in renewable power generation, responsive demand (including data centers and electrolysis plants), electrical and nonelectrical energy conversion and storage, and flexible compensation devices.

HVDC-HVAC power systems dominated by power electronic converter interfaced elements are exposed to frequency instability risk due to low levels of system inertia and ineffective primary frequency control. Hence, significant research is devoted to new control concepts to enable fast active power (P) - frequency support by power electronic converters. Furthermore, when multiple elements attempt to arrest P imbalances, a coordinated strategy is required to avoid adverse consequences of concurrent and interfering control actions such as steep rate-of-change-of-frequency and over/under frequencies during the frequency containment period. To tackle this issue, in this paper, three different optimization strategies are formulated aiming at a cooperative reaction of fast P controllers attached to the outer control layer of modular multilevel converters (MMCs) applied in HVDC links and proton exchange membrane (PEM) electrolyzers. Each formulation is implemented by combining DIgSILENT PowerFactory 2024 SP2 as a power system simulation tool with a Python-based optimization solver that adopts the mean variance mapping optimization algorithm (MVMO). A comparative analysis performed on a multi-area multi-energy hybrid HVAC-HVDC power system to evaluate the proposed formulations in terms of their applicability, effectiveness according to P reserve availability, optimization convergence rate, and the suitability of the frequency response due critical sudden P imbalances.

The increasing penetration of Distributed Energy Resources (DER) expands the cyberattack surface of power systems. This paper analyses, using PowerFactory, the impact and success of MaDIoT 3.0 attacks in the PST-16 model, a simplified model of the European system. MaDIoT 3.0 attacks are a novel type of attack that manage to compromise both high-wattage IoT demand devices and DER devices at the same time. The results indicate that the inclusion of distributed solar PV generation in the PST-16 system reduces the success ratio and impact of load-altering MaDIoT attacks when compared to the same system without DER, mainly due to an increment of the initial voltages. For MaDIoT 3.0 attacks, the demand had a more significant influence on the attack’s success than DER in the PST-16 system. Distributing the attacked demand across more buses or targeting the demand from other areas would decrease the success ratio of the attack. Therefore, the local scalability and replicability of vulnerable high-wattage demand devices in the analysed system become more critical than their distributed deployment in larger areas.

Power system operators need new, efficient operational tools to use the flexibility of distributed resources and deal with the challenges of highly uncertain and variable power systems. Transmission system operators can consider the available flexibility in distribution systems (DSs) without breaching the DS constraints through flexibility areas. However, there is an absence of open-source packages for flexibility area estimation. This paper introduces TensorConvolutionPlus, a user-friendly Python-based package for flexibility area estimation. The main features of TensorConvolutionPlus include estimating flexibility areas using the TensorConvolution+ algorithm, the power flow-based algorithm, an exhaustive PF-based algorithm, and an optimal power flow-based algorithm. Additional features include adapting flexibility area estimations from different operating conditions and including flexibility service providers offering discrete setpoints of flexibility. The TensorConvolutionPlus package facilitates a broader adaptation of flexibility estimation algorithms by system operators and power system researchers.

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.

Coordination between power system operators can improve the power system stability and effectively deploy resources in distribution systems (DS). The research work of this paper provides a coordination method to mitigate the impact of dynamic events on transmission systems (TS). The proposed method uses a machine learning (ML)-based model to estimate the collective dynamic response of DS under varying TS dynamic properties, DS operating conditions, and share of inverter base resources (IBRs). In addition, the ML-based model enables TS operators (TSOs) to provide feedback to DS operators (DSOs) for controlling the IBRs’ active power output to prevent post-fault instabilities. The proposed TSO-DSO coordination method includes a risk-based active power setpoint optimizer for instability prevention. The proposed method uses existing measurement and IBR control platforms available in DS and estimates the post-fault DS dynamic response considering IBR active power control actions. Case studies on synthetic models of TS and DS covering the Zeeland province in The Netherlands illustrate the application of the proposed coordination and the instability risk mitigation when optimizing IBR setpoints.

As the Netherlands moves toward climate neutrality by 2050, the national power system will rely heavily on variable renewable energy sources (VRES) such as offshore wind and solar photovoltaics. While previous studies have examined steady-state implications of overplanting and grid reinforcement, less attention has been given to assessing the effectiveness of flexibility resources during longer time periods. This paper presents an operational assessment of the 2050 Dutch transmission system using full-day optimal power flow simulations for typical summer and winter conditions.The synthetic model of the transmission system is developed in DIgSILENT PowerFactory and includes distributed and centralized supply, batteries, electrolyzers, and demand response mechanisms. Using the Mean-Variance Mapping Optimization (MVMO) algorithm with 15-minute resolution, system operation is optimized to minimize active power losses while respecting voltage and thermal limits. The results show that flexibility resources are essential to ensure demand coverage and reduce transmission congestion, especially during periods of high VRES generation. In winter, the centralized nature of offshore wind leads to regional overloads and higher losses, while summer benefits from decentralized PV generation and more balanced load matching. Batteries and hydrogen units show distinct operational patterns, emphasizing the importance of their strategic placement. These findings support the design of control strategies and infrastructure planning for high-VRES transmission systems.

Converter-interfaced renewable generation predominates in the development of new power system architectures, particularly in offshore systems. The increase of such multi-converter systems leads to the introduction of a new interaction among the different elements of the system and, therefore, new dynamic phenomena. Such phenomenon is subsynchronous and supersynchronous oscillations (SSO) which result, among other causes, from the interaction of converters with weak networks, such as offshore power systems. Various factors, including the challenge of obtaining analytical models from converter-based generation manufacturers and analysing system measurements during planning and operations, necessitate effective measurement-based methods for the swift and numerically trustworthy identification of various characteristics of SSO. Therefore, this paper analyses the advantages and disadvantages of the Dynamic Mode Decomposition method for identifying SSO. The theoretical background of the technique and the application algorithm are presented. The method is first applied to several synthetic signals exhibiting subsynchronous and supersynchronous modes under various conditions, including noise and different time windows. Then, a converted-based resource connected to an infinite bus is presented, and the method is applied to a group of recorded signals from this system under an external perturbation. This method is proposed as an alternative for analysing SSO in converted-based systems due to its ability to assess non-linear systems and its robustness against noise.

Energy production hubs are emerging as a solution to stabilize power grids that are increasingly being challenged by renewable energy sources. The deployment of grid-forming inverters (GFMIs) inherently involves grid regulation tasks such as voltage and frequency control. Such control is distinctly advantageous over the passive grid-following inverter. GFMIs can actively stabilize the grid, but their introduction necessitates a coupling reactance to facilitate voltage and current control. Autonomous voltage and frequency control requires real-time coordination. However, applying MPC is complex due to the multiscale nature of the control problem. To overcome these challenges, this paper proposes a combined controller-hub design where a three-layer hierarchical MPC scheme controls an energy production hub comprised of an integrated energy storage system, a wind turbine, and a GFMI. By decomposing the problem into three distinct layers, the upper two layers can operate in non-real time and require only the bottom layer to work in real time. By designing the middle layer with a novel approach, we investigate how the coupling reactance dynamics affect the power setpoint determination of the energy production hub. The goal is to facilitate control over the grid's active and reactive power flows, voltage, and frequency. As the angle-based droop control law governs the coupling reactance dynamics, we study its incorporation into the MPC objective function and its effect on frequency stability. A simulation study shows how the droop control element alters the power setpoints in the middle layer to compensate for such frequency fluctuations. The results suggest that the hub and controller can reliably provide power from an uncontrolled, sustainable source while providing local stability to the energy grid.

The integration of renewable energy sources and offshore wind farms demands robust High-Voltage Direct Current (HVDC) networks. A key challenge is mitigating post-fault oscillations during converter deblocking, which arise from interactions between converter dynamics, HVDC cables, and system nonlinearities. These oscillations can destabilize the system, extend recovery times, and disrupt grid operations. This study investigates a four-terminal Multiterminal DC (MTDC) network using a real-time simulator. An enhanced DC voltage regulation strategy is proposed, integrating a washout filter and an anti-windup mechanism within a Proportional-Integral (PI) controller. Furthermore, a meticulous parametric sensitivity analysis is performed to optimize controller parameters, achieving significant reductions in oscillations using a real-time simulator to extract valuable insights into the damping method's effectiveness under various operating conditions.

Although several data-driven approaches for short-term voltage stability (STVS) assessment have been proposed, most of them do not extend to corrective control actions nor consider the joint dynamics of generation and load. To address this gap, this work introduces a real-time adaptive load shedding scheme (ALSS) driven by an integrated assessment of the short-term stability state (STSS) and the identification of critical induction motors (CIM) as the mechanism driving STVS instability. The methodology employs two recurrent convolutional neural network (RCNN) models operating in parallel: i) the STSS-RCNN, which classifies the system state as stable, unstable by transient stability (TS), or unstable by STVS; and ii) the CIM-RCNN, which identifies the critical motors responsible for instability, thereby inherently recognizing STVS-related problems. The joint operation of these models ensures that the ALSS is activated only when both responses consistently recognize an STVS event. This enables not only the correct activation of the load shedding scheme but also its accuracy and adaptive parameterization based on the identified CIMs. Validation on the IEEE 39-bus test system demonstrates that the proposed approach achieves robust real-time performance, outperforms single deep learning baselines, and significantly overcomes traditional load shedding schemes in efficiency and reliability.

This paper presents a pivotal stability analysis of the Dutch power system within the context of increased renewable energy integration, employing multiple future scenarios to navigate the inherent uncertainties. A large-scale synthetic model, utilizing ENTSOE-E reference data, uses time-domain simulations and eigenvalue analysis to assess the influence of systemic inertia and kinetic energy on the power system's dynamic frequency and angular stability. The study identifies specific inertia and kinetic energy projections that could undermine the stability of the Dutch power system and potentially affect the continental European power system. It also discusses potential enhancements, including supplementary damping control, to improve the primary control functions of power electronics interfaced generation. The results highlight the critical need for power system planners and operators to take proactive steps to prevent instabilities, ensuring that renewable energy integration strengthens rather than compromises power system reliability.

The increasing deployment of offshore wind farms necessitates robust and stable high-voltage direct current networks. Achieving optimal stability, especially in damping oscillations on the DC side, remains a significant challenge. This study focuses on mitigating post-fault converter de-blocking oscillations, a critical issue exacerbated by complex interactions between AC and DC systems, converter dynamics, and system faults. These behavior are governed by nonlinear system dynamics, making traditional control methods less effective in ensuring stability. A comprehensive analysis of DC side oscillations and their interaction with converter dynamics is developed to understand the key factors influencing system stability. The research investigates a DC voltage regulation damping approach, identified as the most effective solution in the literature. Comprehensive parametric sensitivity analysis evaluates system behavior under diverse operational conditions. Addressing current damping method limitations during converter de-blocking, this work proposes an innovative control approach integrating fuzzy logic control and proportional–integral controllers. This approach enhances DC voltage regulation and incorporates a modified circulating current suppression control in the inner loop. The coordinated fuzzy logic control and proportional–integral controller dynamically adjusts to nonlinear system dynamics in real-time, providing a robust framework for improved post-fault recovery. It aims to achieve faster recovery times and reduced overshoot compared to conventional methods. The proposed controller's efficacy is validated through comparative analysis with existing approaches. Electromagnetic transient) simulations using the real-time digital simulator platform demonstrate the controller's performance under realistic operating conditions.