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.

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.

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.

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 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.

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.

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.

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 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.

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.

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.

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.

This study investigates the dynamic performance of hybrid power systems, with a focus on Multi-Terminal Direct Current (MTDC) interconnected offshore-onshore systems, under various disturbances. Conventional performance metrics such as Rate of Change of Frequency (RoCoF), commonly used for AC systems, are utilized to assess frequency response. Additionally, a modified Rate of Change of Voltage (RoCoV) metric is proposed to capture DC voltage behavior. The effectiveness of these metrics is evaluated through simulations involving various disturbances, including generator outages, line outages, converter outages, and faults. The results demonstrate the ability of the proposed metrics to effectively capture the impact of disturbances on system response, while also identifying limitations in capturing oscillating responses. Furthermore, the parametric sensitivity of control parameters in the converter’s outer control loop is analyzed to assess their influence on system behavior.

The Nordic Power System is undergoing significant transformation and development in the coming years. A driving factor for this is the increased dependency on renewable energy sources and high voltage direct current transmission (HVDC). Various stability challenges due to the dynamics of reactive power - voltage (Q-V) balancing and active power - frequency (P-f) balancing seep into the system with such rise of non-synchronous generation (NSG). This paper investigates the projected impact of NSGs on the P-f dynamic performance of Nordic power system in 2030. As NSGs are systems with low inertia, power discrepancies lead to larger oscillations, higher Rate of Change of Frequency (RoCoF) and bigger maximum frequency deviation (MFD) values. To address these issues, Emulated Inertia (EI) control for wind turbines and Synthetic Inertia (SI) control for VSC-HVDC links are proposed in this paper. The simulation is carried out using DIgSILENT PowerFactory 2022 SP1 simulation software. The results obtained confirm the Effectiveness of the proposed EI and SI methods to enhance frequency response and mitigate deviations.

As modern trends demand efficient methods to massively and promptly exploit clean energy, there is an urgent need of solutions that can help the ambition of accelerating the development and integration of larger scale energy systems with the capacity to extract, store, and utilize huge amounts of renewable energy sources. In view of this, the rated power output of the wind generation technology is expected to significantly increase in the near future to enable multi-gigawatt roll outs, e.g. the European plans for Offshore infrastructure development in the North-Sea. Besides, gigantic energy systems shall transmit the power to onshore interconnected systems via HVDC transmission. This paper introduces an EMT model of a 20 MW wind generation system with the conventional back-to-back converter replaced by a combination of an AC to DC converter and a DC to DC converter. While the need of such systems is acknowledged in the literature, the design and performance analysis of high power implementation of such systems have not been addressed yet. The proposed system operates at a voltage level of 100 kV, which can directly transfer power through HVDC interconnection. The designed control aspects are outlined and the resulting dynamic response obtained from realtime digital simulation (RTDS) are discussed. The presented wind generator concept can help in reducing the size of a planned energy system by requiring lesser system components. Also, the system efficiency can be enhanced by decreasing the number of power conversion stages.

This work seeks to reduce the severity of congestion in the medium voltage (MV) cyber-physical systems (CPES) by optimising the network topology in line with seasonal variations in the supply and demand of electricity. To this aim a two-stage reconfiguration algorithm is proposed. In the first stage, the positions of the normally open switches within the network are optimised in to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a mixed-integer linear program (MILP) optimisation in Python, whereas the second stage consists of a Newton-Raphson calculation in DIgSILENT PowerFactory software package. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by a distribution system operator (DSO) without relying on other external parties. The presented case study addresses the need of seasonal reconfigurations to accommodate for the manual operation of the switches present within a synthetic model of MV CPES, which is implemented inspired from CPES in the Netherlands. The application of the algorithm significantly reduces the frequency by which reconfiguration actions can be performed. Furthermore, the algorithm is able to consistently reduce congestion within the analysed synthetic CPES, completely removing it or reducing its severity. It also outperforms two alternative optimisation options implemented in PowerFactory with regard to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.

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.

Power grid digitalization introduces new vulnerabilities and cyber security threats. The impact of cyber attacks on power system stability is a topic of growing concern, which is yet to be comprehensively analyzed. Traditional power system stability analysis is based on the impact of non-malicious small, and large physical disturbances. However, cyber attacks introduce a new dimension to power system stability, in which malicious cyber actors can selectively target critical systems and applications and cause severe stability issues. Hence, in this work, the traditional disturbances considered in power system stability classification are expanded from physical to cyber-physical disturbances caused by cyber attacks. Based on a thorough state-of-the-art, an analysis of how cyber attacks can translate into physical disturbances affecting the traditional power system stability categories is performed. The system stability analysis is expanded by mapping the power system stability categories with the defined cyber-physical attack types. The findings of this work showcase the importance of cyber security for power system stability.

One crucial aspect of Modular Multi-level Converter (MMC)- Bipolar Point-to-Point (BPP) configuration systems is the occurrence and damping of oscillations on the DC side of HVDC networks. These oscillations can arise due to various factors, including the interaction between the AC and DC systems, de-blocking of converters after a fault, and the dynamic behaviour of connected power sources. Various investigations consider damping methods that delve to mitigate oscillatory tendencies and establish stability during Post-Fault (PF) recovery. However, the current research on damping predominantly focus on the impact of AC fault or unbalanced conditions on the DC side. This paper presents an investigation that addresses the gap concerning with sub-synchronous oscillations occurring during the de-blocking of a MMC-BPP within the post-DC fault recovery. The investigation also considers active damping and enhanced current control loop as a combined mitigation measure. A meticulous real-time digital simulation supported parametric sensitivity analysis is conducted on a four-terminal MMC-BPP synthetic power system. Numerical results provide insight into the level of effectiveness that can be achieved by the considered concept of active damping.

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.