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

Maria Stefan; Jose Manuel Gutierrez Lopez; Rasmus Løvenstein Olsen

doi:10.1109/BigData.2018.8622154

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

Maria Stefan, Jose Manuel Gutierrez Lopez, Rasmus Løvenstein Olsen

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2018 IEEE International Conference on Big Data (Big Data)
Editors	Yang 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
Number of pages	6
Publisher	IEEE
Publication date	24 Jan 2019
Pages	3543-3548
Article number	8622154
ISBN (Print)	978-1-5386-5036-3
ISBN (Electronic)	978-1-5386-5035-6
DOIs	https://doi.org/10.1109/BigData.2018.8622154
Publication status	Published - 24 Jan 2019
Event	2018 IEEE International Conference on Big Data - Seattle, United States Duration: 10 Dec 2018 → 13 Dec 2018

Conference

Conference	2018 IEEE International Conference on Big Data
Country/Territory	United States
City	Seattle
Period	10/12/2018 → 13/12/2018

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10.1109/BigData.2018.8622154

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Stefan, M., Gutierrez Lopez, J. M., & Olsen, R. L. (2019). Exploring the Potential of Modern Advanced Metering Infrastructure in Low-Voltage Grid Monitoring Systems. In 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 (Eds.), 2018 IEEE International Conference on Big Data (Big Data) (pp. 3543-3548). Article 8622154 IEEE. https://doi.org/10.1109/BigData.2018.8622154

Stefan, Maria ; Gutierrez Lopez, Jose Manuel ; Olsen, Rasmus Løvenstein. / Exploring the Potential of Modern Advanced Metering Infrastructure in Low-Voltage Grid Monitoring Systems. 2018 IEEE International Conference on Big Data (Big Data). editor / Yang 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. IEEE, 2019. pp. 3543-3548

@inproceedings{3c063fba9bd543f3a9dc39bc16821ac0,

title = "Exploring the Potential of Modern Advanced Metering Infrastructure in Low-Voltage Grid Monitoring Systems",

abstract = "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.",

author = "Maria Stefan and {Gutierrez Lopez}, {Jose Manuel} and Olsen, {Rasmus L{\o}venstein}",

year = "2019",

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doi = "10.1109/BigData.2018.8622154",

language = "English",

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booktitle = "2018 IEEE International Conference on Big Data (Big Data)",

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note = "2018 IEEE International Conference on Big Data ; Conference date: 10-12-2018 Through 13-12-2018",

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Stefan, M, Gutierrez Lopez, JM & Olsen, RL 2019, Exploring the Potential of Modern Advanced Metering Infrastructure in Low-Voltage Grid Monitoring Systems. in 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 (eds), 2018 IEEE International Conference on Big Data (Big Data)., 8622154, IEEE, pp. 3543-3548, 2018 IEEE International Conference on Big Data, Seattle, Washington, United States, 10/12/2018. https://doi.org/10.1109/BigData.2018.8622154

Exploring the Potential of Modern Advanced Metering Infrastructure in Low-Voltage Grid Monitoring Systems. / Stefan, Maria; Gutierrez Lopez, Jose Manuel ; Olsen, Rasmus Løvenstein.
2018 IEEE International Conference on Big Data (Big Data). ed. / Yang 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. IEEE, 2019. p. 3543-3548 8622154.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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T1 - Exploring the Potential of Modern Advanced Metering Infrastructure in Low-Voltage Grid Monitoring Systems

AU - Stefan, Maria

AU - Gutierrez Lopez, Jose Manuel

AU - Olsen, Rasmus Løvenstein

PY - 2019/1/24

Y1 - 2019/1/24

N2 - 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.

AB - 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.

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U2 - 10.1109/BigData.2018.8622154

DO - 10.1109/BigData.2018.8622154

M3 - Article in proceeding

SN - 978-1-5386-5036-3

SP - 3543

EP - 3548

BT - 2018 IEEE International Conference on Big Data (Big Data)

A2 - Song, Yang

A2 - Liu, Bing

A2 - Lee, Kisung

A2 - Abe, Naoki

A2 - Pu, Calton

A2 - Qiao, Mu

A2 - Ahmed, Nesreen

A2 - Kossmann, Donald

A2 - Saltz, Jeffrey

A2 - Tang, Jiliang

A2 - He, Jingrui

A2 - Liu, Huan

A2 - Hu, Xiaohua

PB - IEEE

T2 - 2018 IEEE International Conference on Big Data

Y2 - 10 December 2018 through 13 December 2018

ER -

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

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

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