The integration of large amounts of generation into distribution networks faces some limitations. By deploying reactive power-based voltage control concepts (e.g., volt/var control with distributed generators), the voltage rise caused by generators can be partly mitigated. As a result, the network hosting capacity can be accordingly increased, and costly network reinforcement might be avoided or postponed. This works however only for voltage-constrained feeders (opposed to current-constrained feeders). Due to the low level of monitoring in low voltage networks, it is important to be able to classify feeders according to the expected constraint in order to avoid the overloading risk. The main purpose of this paper is to investigate to which extent it is possible to predict the hosting capacity constraint (voltage or current) of low voltage feeders on the basis of a large network data set. Two machine-learning techniques have been implemented and compared: clustering (unsupervised) and classification (supervised). The results show that the general performance of the classification or clustering algorithms might be considered as rather poor at a first glance, reflecting the diversity of real low voltage feeders. However, a detailed analysis shows that the benefit of the classification is significant.
Dynamic simulations have played an important role in assessing the power system dynamic studies. The appropriate numerical model is the key to obtain correct dynamic simulation results. In addition, the appropriate model including the selection of the individual model component (such as protections, controls and capabilities) is different depending on the type of phenomena to be observed or examined. However, the proper selection of the model is not an easy task especially for Inverter Based Generators (IBGs). Considerable industry experience concerning power system dynamic studies and the dynamics of the IBGs is required for the proper selection of the IBG model. The established CIGRE C4/C6.35/CIRED Joint Working Group (JWG) has gathered a wide variety of experts which fully cover the required industry experience. The JWG provides the guidance on the model selection for analyzing the phenomena such as frequency deviation, large voltage deviation, and long-term voltage deviation, individually. This helps to reduce the computational burden as well as it clarifies the required characteristics/functions that should be represented for the power system dynamic studies with the IBGs.
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