Exploring the Potential of Modern Advanced Metering Infrastructure in Low-Voltage Grid Monitoring Systems

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Energy systems are evolving towards 100% green energy production. The share of green energy in electrical distribution systems is progressively increasing, implying also an increment on the number of renewable energy units in the low-voltage grid. Following this trend, thousands of consumers connected to the power grid in a decentralized manner become small producers, changing the traditional paradigm of energy distribution from top to bottom. Currently, the modern Advanced Metering Infrastructure (AMI) enables the possibility of collecting several types of status data from the end-users which Distribution System Operators (DSOs) can use to their advantage to optimize management and planning operations. As a part of this optimization, having a spatial overview over the low-voltage grid can speed up the monitoring processes and allows to obtain a real-time insight on what is happening in the grid, compared to the traditionally used analysis methods. Many business structures for smart grid cyber physical systems are looking into how to integrate advanced data management models. Such models should provide the means for obtaining meaningful data visualization where only the relevant data is timely processed, filtered and visualized for the operators to efficiently react to grid anomalies in real-time.The purpose of this paper is to investigate how to efficiently design a monitoring/visualization system for low-voltage electrical grids based on the DSOs' needs and feedback. The proposed system implementation stands on emulating an existing geographic scenario by a virtual AMI integration. The efficiency of the prototype is evaluated versus the traditional monitoring operations derived from user experience studies, such as a reduction in time to perform a specific anomaly detection operation. Furthermore, the advantages of spatial awareness are meant to further strengthen the motivation for integrating measurements into a Geographic Information System (GIS) environment.
Titel2018 IEEE International Conference on Big Data (Big Data)
RedaktørerYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
Antal sider6
Publikationsdato24 jan. 2019
ISBN (Trykt)978-1-5386-5036-3
ISBN (Elektronisk)978-1-5386-5035-6
StatusUdgivet - 24 jan. 2019
Begivenhed2018 IEEE International Conference on Big Data - Seattle, USA
Varighed: 10 dec. 201813 dec. 2018


Konference2018 IEEE International Conference on Big Data

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  • Citationsformater

    Stefan, M., Gutierrez Lopez, J. M., & Olsen, R. L. (2019). Exploring the Potential of Modern Advanced Metering Infrastructure in Low-Voltage Grid Monitoring Systems. I Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, & X. Hu (red.), 2018 IEEE International Conference on Big Data (Big Data) (s. 3543-3548). [8622154] IEEE. https://doi.org/10.1109/BigData.2018.8622154