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Next Generation Real-Time Smart Meters for ICT Based Assessment of Grid Data Inconsistencies

Author

Listed:
  • Mihai Sanduleac

    (Romanian Energy Center, 011838 Bucharest, Romania
    Department of Electrical Power Systems, University Politehnica of Bucharest, 060042 Bucharest, Romania)

  • Gianluca Lipari

    (Rheinisch-Westfälische Technische Hochschule Aachen—RWTH, 52062 Aachen, Germany)

  • Antonello Monti

    (Rheinisch-Westfälische Technische Hochschule Aachen—RWTH, 52062 Aachen, Germany)

  • Artemis Voulkidis

    (Synelixis, GR-34100 Chalkida, Greece)

  • Gianluca Zanetto

    (R&D Department, Teamware, 20128 Milan, Italy)

  • Antonello Corsi

    (Engineering Ingegneria Informatica S.p.A, Engineering, 00148 Rome, Italy)

  • Lucian Toma

    (Department of Electrical Power Systems, University Politehnica of Bucharest, 060042 Bucharest, Romania)

  • Giampaolo Fiorentino

    (Engineering Ingegneria Informatica S.p.A, Engineering, 00148 Rome, Italy)

  • Dumitru Federenciuc

    (Department of Strategy, Electrica, 010621 Bucharest, Romania)

Abstract

The latest technological developments are challenging for finding new solutions to mitigate the massive integration of renewable-based electricity generation in the electrical networks and to support new and dynamic energy and ancillary services markets. Smart meters have become ubiquitous equipment in the low voltage grid, enabled by the decision made in many countries to support massive deployments. The smart meter is the only equipment mandatory to be mounted when supplying a grid connected user, as it primarily has the function to measure delivered and/or produced energy on its common coupling point with the network, as technical and legal support for billing. Active distribution networks need new functionalities, to cope with the bidirectional energy flow behaviour of the grid, and many smart grid requirements need to be implemented in the near future. However there is no real coupling between smart metering systems and smart grids, as there is not yet a synergy using the opportunity of the high deployment level in smart metering. The paper presents a new approach for managing the smart metering and smart grid orchestration by presenting a new general design based on an unbundled smart meter (USM) concept, labelled as next generation open real-time smart meters (NORM), for integrating the smart meter, phasor measurement unit (PMU) and cyber-security through an enhanced smart metering gateway (SMG). NORM is intended to be deployed everywhere at the prosumer’s interface to the grid, as it is usually now done with the standard meter. Furthermore, rich data acquired from NORM is used to demonstrate the potential of assessing grid data inconsistencies at a higher level, as function to be deployed in distribution security monitoring centers, to address the higher level cyber-security threats, such as false data injections and to allow secure grid operations and complex market activities at the same time. The measures are considering only non-sensitive data from a privacy perspective, and is therefore able to be applied everywhere in the grid, down to the end-customer level, where a citizen’s personal data protection is an important aspect.

Suggested Citation

  • Mihai Sanduleac & Gianluca Lipari & Antonello Monti & Artemis Voulkidis & Gianluca Zanetto & Antonello Corsi & Lucian Toma & Giampaolo Fiorentino & Dumitru Federenciuc, 2017. "Next Generation Real-Time Smart Meters for ICT Based Assessment of Grid Data Inconsistencies," Energies, MDPI, vol. 10(7), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:857-:d:102721
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    References listed on IDEAS

    as
    1. Neetesh Saxena & Bong Jun Choi, 2015. "State of the Art Authentication, Access Control, and Secure Integration in Smart Grid," Energies, MDPI, vol. 8(10), pages 1-33, October.
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    5. Alessandro Pitì & Giacomo Verticale & Cristina Rottondi & Antonio Capone & Luca Lo Schiavo, 2017. "The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case," Energies, MDPI, vol. 10(2), pages 1-25, February.
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    Cited by:

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    2. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
    3. Giovanni Artale & Giuseppe Caravello & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Salvatore Guaiana & Ninh Nguyen Quang & Marco Palmeri & Nicola Panzavecchia & Giovanni Tinè, 2020. "A Virtual Tool for Load Flow Analysis in a Micro-Grid," Energies, MDPI, vol. 13(12), pages 1-26, June.
    4. Chong Chen & Xuan Zhou & Xiaowei Yang & Zhiheng He & Zhuo Li & Zhengtian Li & Xiangning Lin & Ting Wen & Yixin Zhuo & Ning Tong, 2018. "Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads," Energies, MDPI, vol. 11(4), pages 1-22, April.
    5. Kai Ma & Yege Bai & Jie Yang & Yangqing Yu & Qiuxia Yang, 2017. "Demand-Side Energy Management Based on Nonconvex Optimization in Smart Grid," Energies, MDPI, vol. 10(10), pages 1-17, October.

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