This paper deals with the small-signal stability analysis of converter control modes in low-inertia power systems. For this purpose, a detailed differential-algebraic equation model of the voltage source converter and its control scheme is developed. Both grid-forming and grid-feeding concepts have been considered, as well as different active power controllers based on traditional droop and virtual inertia emulation. An eigenvalue analysis of the linearized state-space system is conducted and the performance of different control configurations is compared. Furthermore, various bifurcation studies have been completed and conclusions on stability margins have been drawn with respect to control sensitivity and robustness.

No abstract

In modern power systems, shiftable loads contribute to the flexibility needed to increase robustness and ensure security. Thermal loads are among the most promising candidates for providing such service due to the large thermal storage time constants. This study demonstrates the use of variable-speed refrigeration (VSR) technology, based on brushless DC motors, for the fast-frequency response. First, the authors derive a detailed dynamic model of a single-phase VSR unit suitable for time-domain and small-signal stability analysis in low-inertia systems. For analysing dynamic interactions with the grid, they consider the aggregated response of multiple devices. However, the high computational cost involved in analysing large-scale systems leads to the need for reduced-order models. Thus, a set of reduced-order models is derived through transfer function fitting of data obtained from time-domain simulations of the detailed model. The modelling requirements and the accuracy versus computational complexity trade-off are discussed. Finally, the time-domain performance and frequency-domain analyses reveal substantial equivalence between the full-and suitable reduced-order models, allowing the application of simplified models in large-scale system studies.

No abstract

While distribution networks (DNs) turn from consumers to active and responsive intelligent DNs, the question of how to represent them in large-scale transmission network (TN) studies is still under investigation. The standard approach that uses aggregated models for the inverter-interfaced generation and conventional load models introduces significant errors to the dynamic modeling that can lead to instabilities. This paper presents a new approach based on quantile forecasting to represent the uncertainty originating in DNs at the TN level. First, we aquire a required rich dataset employing Monte Carlo simulations of a DN. Then, we use machine learning (ML) algorithms to not only predict the most probable response but also intervals of potential responses with predefined confidence. These quantile methods represent the variance in DN responses at the TN level. The results indicate excellent performance for most ML techniques. The tuned quantile equivalents predict accurate bands for the current at the TN/DN-interface, and tests with unseen TN conditions indicate robustness. A final assessment that compares the MC trajectories against the predicted intervals highlights the potential of the proposed method.