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Energies 2017, 10(8), 1099;

A Large Scale Grid Data Analysis Platform for DSOs

Center for Energy - Electric Energy Systems, AIT Austrian Institute of Technology, Vienna 1210, Austria
Institute of Energy Systems and Electrical Drives, TU Wien, Vienna 1040, Austria
Author to whom correspondence should be addressed.
Received: 7 June 2017 / Revised: 14 July 2017 / Accepted: 14 July 2017 / Published: 27 July 2017
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The number of fluctuating distributed energy resources (DER) in electricity grids is continuously rising. Due to the lack of operational information on low-voltage (LV) networks, conservative assumptions are necessary to assess the connection of generators to the grid. This paper introduces the hosting capability (HC) as a measure to assess the amount of DER that can be integrated in LV-feeders. The HC of a feeder is the minimum amount of DER that can be hosted in a feeder without reinforcement needs for a given DER-scenario and for a given admissible voltage rise. The hosting capability assessment was performed on the entire LV-grid data of two Austrian Distribution System Operators (DSOs) with more than 36,000 LV-feeders. In total, 40 HC-scenarios were calculated with varying admissible voltage rise levels, DER-scenarios and reactive power control strategies. It turned out that only few feeder parameters such as the resistance at the end node and the lowest ampacity value of feeders show a high correlation with the calculated HC. Further, the impact of the DER-scenario on the share of voltage and loading constrained feeders is rather limited. The gathered results are suitable to validate equivalent LV-feeders models to perform integrated power flow studies on the transmission and distribution grids. Besides the results obtained for the network data of the two DSOs, a performant, modular and parallelizable tool has been developed to automatically analyze large LV network sets. View Full-Text
Keywords: low-voltage networks; hosting capacity; scalability; replicability analysis low-voltage networks; hosting capacity; scalability; replicability analysis

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Kadam, S.; Bletterie, B.; Gawlik, W. A Large Scale Grid Data Analysis Platform for DSOs. Energies 2017, 10, 1099.

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