NDLI: Near real-time electric load approximation in low voltage cables of smart grids with models@run.time

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Near real-time electric load approximation in low voltage cables of smart grids with models@run.time

Content Provider	ACM Digital Library
Author	Hartmann, Thomas Klein, Jacques Reckinger, Yves Moawad, Assaad Le Traon, Yves Fouquet, Francois
Abstract	Micro-generations and future grid usages, such as charging of electric cars, raises major challenges to monitor the electric load in low-voltage cables. Due to the highly interconnected nature, real-time measurements are problematic, both economically and technically. This entails an overload risk in electricity networks when cables must be disconnected for maintenance reasons or are accidentally damaged. Therefore, it is of great interest for electricity grid providers to anticipate the load in networks and quicker detect failures. However, computing the electric load in cables requires computational intensive power flow calculations and live consumption measurements. Today's view of the grid is usually based on on-field documentation of cables, fuses, and measurements by technicians and therefore often outdated. Thus, the electric load is usually only simulated in case of major topology variations. However, live measurements of smart meters provide new opportunities. In this paper we present a novel approach for a near-time electric load approximation by deriving in live the current electric topology and cable loads from smart meter data. We leverage the models@run.time paradigm to combine live measurements with topology characteristics of the grid. Our approach enables to approximate the load in cables, not only for the current grid topology, but also to simulate topology changes for maintenance purposes. We showed that this allows a near real-time approximation while remaining very accurate (average deviation of 1.89% compared to offline power-flow calculation tools). Developed with a grid operator, this approach will be integrated in a monitoring and warning system and as an embeddable solution for on-field simulation.
Starting Page	2119
Ending Page	2126
Page Count	8
File Format	PDF
ISBN	9781450337397
DOI	10.1145/2851613.2853125
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-04-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Model-driven engineering Models@run.time Electric load prediction Smart grid Real-time monitoring
Content Type	Text
Resource Type	Article

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