When multiple grid-forming and grid-following converters operate within an offshore energy system (OES), dynamic interactions among them can lead to poorly damped oscillations and potential instability. This paper proposes a computationally efficient reduced-order state-space modelling framework for small-signal stability and parametric sensitivity analysis of large-scale OESs. The approach replaces complex full-order analytical modelling with a practical MATLAB/Simulink-based linearisation procedure, enabling tractable stability assessment of systems comprising multiple wind plants, electrolysers, HVDC links and network components. The reduced-order model preserves the dominant dynamics while significantly decreasing the number of states, thereby improving computational efficiency for eigenvalue and sensitivity analyses. Using the linearised model, the influence of key control parameters is systematically quantified to provide explicit guidance for controller tuning and damping improvement. The accuracy of the proposed model is validated through comparison with detailed electromagnetic transient (EMT) simulations in PSCAD/EMTDC, demonstrating close agreement in dynamic responses and stability characteristics.

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.

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.

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.