Quantifying the uncertainty imposed by inaccurate modeling of active distribution grids

Replacing conventional generation with inverter-interfaced units has turned distribution networks (DNs) from consumers to active and responding intelligent DNs. These modern DNs contain several devices that can support the transmission network (TN) and system stability. Typically, deterministic and aggregated models for inverter-interfaced generation and conventional loads are used to include entire DNs in bulk system stability studies, and contributions from smart loads are neglected. This approach introduces errors to the dynamic modeling that can lead to instabilities. In this paper, we first present a full detailed model of a modern DN, enhancing existing thermal load and distributed generation models to include frequency and voltage support and protection functions required in low-inertia systems. Then, we incorporate the uncertainty that stems from the parameterization of such units using a Monte-Carlo method. Finally, we assess the impact of neglecting specific protection and support functions against frequency disturbances. The results show the crucial importance of accurately modeling protection and support functions to analyze the impact of modern DNs on bulk system stability. In addition, the findings highlight the increased relevance of considering uncertainty in stability studies of weak and low-inertia power systems.ISSN:0378-7796ISSN:1873-204

Using Quantile Forecasts for Dynamic Equivalents of Active Distribution Grids under Uncertainty

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

Modelling of variable-speed refrigeration for fast-frequency control in low-inertia systems

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