IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3730-d386996.html
   My bibliography  Save this article

Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review

Author

Listed:
  • Antonio E. Saldaña-González

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Universitat Politècnica de Catalunya ETS d’Enginyeria Industrial de Barcelona, Avinguda Diagonal, 647, Pl. 2, 08028 Barcelona, Spain)

  • Andreas Sumper

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Universitat Politècnica de Catalunya ETS d’Enginyeria Industrial de Barcelona, Avinguda Diagonal, 647, Pl. 2, 08028 Barcelona, Spain)

  • Mònica Aragüés-Peñalba

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Universitat Politècnica de Catalunya ETS d’Enginyeria Industrial de Barcelona, Avinguda Diagonal, 647, Pl. 2, 08028 Barcelona, Spain)

  • Miha Smolnikar

    (COMSENSUS, Brezje pri Dobu 8, 1233 Dob, Slovenia)

Abstract

The integration of advanced measuring technologies in distribution systems allows distribution system operators to have better observability of dynamic and transient events. In this work, the applications of distribution grid measurement technologies are explored in detail. The main contributions of this review are: (a) a comparison of eight advanced measurement devices for distribution networks, based on their technical characteristics, including reporting periods, measuring data, precision, and sample rate; (b) a review of the most recent applications of micro-Phasor Measurement Units, Smart Meters, and Power Quality Monitoring devices used in distribution systems, considering different novel methods applied for data analysis; and (c) an input-output table that relates measured quantities from micro-Phasor Measurement Units and Smart Meters needed for each specific application found in this extensive review. This paper aims to serve as an important guide for researches and engineers studying smart grids.

Suggested Citation

  • Antonio E. Saldaña-González & Andreas Sumper & Mònica Aragüés-Peñalba & Miha Smolnikar, 2020. "Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review," Energies, MDPI, vol. 13(14), pages 1-34, July.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3730-:d:386996
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3730/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/14/3730/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Flávia P. Monteiro & Suzane A. Monteiro & Maria E. Tostes & Ubiratan H. Bezerra, 2019. "Using True RMS Current Measurements to Estimate Harmonic Impacts of Multiple Nonlinear Loads in Electric Distribution Grids," Energies, MDPI, vol. 12(21), pages 1-21, October.
    2. Sharma, Konark & Saini, Lalit Mohan, 2017. "Power-line communications for smart grid: Progress, challenges, opportunities and status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 704-751.
    3. Ali, S.M. & Jawad, M. & Khan, B. & Mehmood, C.A. & Zeb, N. & Tanoli, A. & Farid, U. & Glower, J. & Khan, S.U., 2016. "Wide area smart grid architectural model and control: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 311-328.
    4. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
    5. Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie, 2016. "Broadband PLC for Clustered Advanced Metering Infrastructure (AMI) Architecture," Energies, MDPI, vol. 9(7), pages 1-19, July.
    6. Xiaoyao Huang & Tianbin Hu & Chengjin Ye & Guanhua Xu & Xiaojian Wang & Liangjin Chen, 2019. "Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders," Energies, MDPI, vol. 12(4), pages 1-17, February.
    7. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    8. Kabalci, Yasin, 2016. "A survey on smart metering and smart grid communication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 302-318.
    9. McKenna, Eoghan & Richardson, Ian & Thomson, Murray, 2012. "Smart meter data: Balancing consumer privacy concerns with legitimate applications," Energy Policy, Elsevier, vol. 41(C), pages 807-814.
    10. Su, Hongzhi & Wang, Chengshan & Li, Peng & Liu, Zhelin & Yu, Li & Wu, Jianzhong, 2019. "Optimal placement of phasor measurement unit in distribution networks considering the changes in topology," Applied Energy, Elsevier, vol. 250(C), pages 313-322.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hun Mun & Byunghoon Moon & Soojin Park & Yongbeum Yoon, 2021. "A Study on the Economic Feasibility of Stand-Alone Microgrid for Carbon-Free Island in Korea," Energies, MDPI, vol. 14(7), pages 1-16, March.
    2. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    3. Giovanni Betta & Domenico Capriglione & Luigi Ferrigno & Marco Laracca & Gianfranco Miele & Nello Polese & Silvia Sangiovanni, 2021. "A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations," Energies, MDPI, vol. 14(22), pages 1-23, November.
    4. Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
    5. Antonio Moretti & Charalampos Pitas & George Christofi & Emmanuel Bué & Modesto Gabrieli Francescato, 2020. "Grid Integration as a Strategy of Med-TSO in the Mediterranean Area in the Framework of Climate Change and Energy Transition," Energies, MDPI, vol. 13(20), pages 1-22, October.
    6. Rizeakos, V. & Bachoumis, A. & Andriopoulos, N. & Birbas, M. & Birbas, A., 2023. "Deep learning-based application for fault location identification and type classification in active distribution grids," Applied Energy, Elsevier, vol. 338(C).
    7. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    8. Dan Liu & Yiqun Kang & Heng Luo & Xiaotong Ji & Kan Cao & Hengrui Ma, 2023. "A Grid Status Analysis Method with Large-Scale Wind Power Access Using Big Data," Energies, MDPI, vol. 16(12), pages 1-12, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Kolasa, Piotr & Janowski, Mirosław, 2017. "Study of possibilities to store energy virtually in a grid (VESS) with the use of smart metering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1513-1517.
    3. Giovanni Artale & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Riccardo Fiorelli & Salvatore Guaiana & Nicola Panzavecchia & Giovanni Tinè, 2019. "A New Coupling Solution for G3-PLC Employment in MV Smart Grids," Energies, MDPI, vol. 12(13), pages 1-23, June.
    4. Arcia-Garibaldi, Guadalupe & Cruz-Romero, Pedro & Gómez-Expósito, Antonio, 2018. "Future power transmission: Visions, technologies and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 285-301.
    5. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    6. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
    7. Alvaro Llaria & Jessye Dos Santos & Guillaume Terrasson & Zina Boussaada & Christophe Merlo & Octavian Curea, 2021. "Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management," Energies, MDPI, vol. 14(9), pages 1-37, May.
    8. Alexis Gerossier & Robin Girard & Alexis Bocquet & George Kariniotakis, 2018. "Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges," Energies, MDPI, vol. 11(12), pages 1-18, December.
    9. Wu, Ying & Wu, Yanpeng & Guerrero, Josep M. & Vasquez, Juan C., 2021. "A comprehensive overview of framework for developing sustainable energy internet: From things-based energy network to services-based management system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    10. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    11. Artur Felipe da Silva Veloso & José Valdemir Reis Júnior & Ricardo de Andrade Lira Rabelo & Jocines Dela-flora Silveira, 2021. "HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service," Future Internet, MDPI, vol. 13(11), pages 1-45, October.
    12. A. Jain & A. Mani & A. S. Siddiqui, 2019. "Network architecture for demand response implementation in smart grid," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(6), pages 1389-1402, December.
    13. Mahmud, Khizir & Khan, Behram & Ravishankar, Jayashri & Ahmadi, Abdollah & Siano, Pierluigi, 2020. "An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    14. Gregorio López & José Ignacio Moreno & Eutimio Sánchez & Cristina Martínez & Fernando Martín, 2017. "Noise Sources, Effects and Countermeasures in Narrowband Power-Line Communications Networks: A Practical Approach," Energies, MDPI, vol. 10(8), pages 1-42, August.
    15. Muhammad Awais Shahid & Fiaz Ahmad & Fahad R. Albogamy & Ghulam Hafeez & Zahid Ullah, 2022. "Detection and Prevention of False Data Injection Attacks in the Measurement Infrastructure of Smart Grids," Sustainability, MDPI, vol. 14(11), pages 1-25, May.
    16. Chou, Jui-Sheng & Gusti Ayu Novi Yutami, I, 2014. "Smart meter adoption and deployment strategy for residential buildings in Indonesia," Applied Energy, Elsevier, vol. 128(C), pages 336-349.
    17. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    18. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    19. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    20. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3730-:d:386996. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.