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Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks

Author

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  • Javier Leiva

    (Endesa, 28042 Madrid, Spain
    Department of Electrical Engineering, University of Málaga, 29016 Málaga, Spain)

  • Rubén Carmona Pardo

    (Endesa, 28042 Madrid, Spain)

  • José A. Aguado

    (Department of Electrical Engineering, University of Málaga, 29016 Málaga, Spain)

Abstract

A growing presence of distributed energy resources (DER) and the increasingly diverse nature of end users at low-voltage (LV) networks make the operation of these grids more and more challenging. Particularly, congestion and voltage management strategies for LV grids have usually been limited to some elemental criteria based on human experience, asset oversizing, or grid reinforcement. However, with the current massive deployment of sensors in modern LV grids, new approaches are feasible for distribution network assets operation. This article proposes a multi-objective particle swarm optimization (MOPSO) approach, combined with data analytics through affinity propagation clustering, for congestion threshold determination in LV grids. A real case study from the smart grid of Smartcity Malaga Living Lab is used to illustrate the proposed approach. Within this approach, distribution system operators (DSOs) can take decisions in order to prevent situations of risk or potential failure at LV grids.

Suggested Citation

  • Javier Leiva & Rubén Carmona Pardo & José A. Aguado, 2019. "Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks," Energies, MDPI, vol. 12(7), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1295-:d:219955
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    References listed on IDEAS

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    1. Carillo-Aparicio, Susana & Heredia-Larrubia, Juan R. & Perez-Hidalgo, Francisco, 2013. "SmartCity Málaga, a real-living lab and its adaptation to electric vehicles in cities," Energy Policy, Elsevier, vol. 62(C), pages 774-779.
    2. Ayman Esmat & Julio Usaola & María Ángeles Moreno, 2018. "Distribution-Level Flexibility Market for Congestion Management," Energies, MDPI, vol. 11(5), pages 1-24, April.
    3. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
    4. Chong Cao & Luting Wang & Bo Chen, 2016. "Mitigation of the Impact of High Plug-in Electric Vehicle Penetration on Residential Distribution Grid Using Smart Charging Strategies," Energies, MDPI, vol. 9(12), pages 1-19, December.
    5. Serdar Kadam & Benoît Bletterie & Wolfgang Gawlik, 2017. "A Large Scale Grid Data Analysis Platform for DSOs," Energies, MDPI, vol. 10(8), pages 1-24, July.
    6. Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2014. "Probabilistic modeling and assessment of the impact of electric heat pumps on low voltage distribution networks," Applied Energy, Elsevier, vol. 127(C), pages 249-266.
    7. Martin Håberg & Hanna Bood & Gerard Doorman, 2019. "Preventing Internal Congestion in an Integrated European Balancing Activation Optimization," Energies, MDPI, vol. 12(3), pages 1-11, February.
    8. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    9. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    10. Tine L. Vandoorn & Jan Van de Vyver & Louis Gevaert & Lieven Degroote & Lieven Vandevelde, 2015. "Congestion Control Algorithm in Distribution Feeders: Integration in a Distribution Management System," Energies, MDPI, vol. 8(6), pages 1-20, June.
    11. Zhongyong Zhao & Chao Tang & Qu Zhou & Lingna Xu & Yingang Gui & Chenguo Yao, 2017. "Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine," Energies, MDPI, vol. 10(12), pages 1-16, December.
    12. Reihani, Ehsan & Motalleb, Mahdi & Ghorbani, Reza & Saad Saoud, Lyes, 2016. "Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration," Renewable Energy, Elsevier, vol. 86(C), pages 1372-1379.
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    Cited by:

    1. Aleksandra Baczyńska & Waldemar Niewiadomski, 2020. "Power Flow Tracing for Active Congestion Management in Modern Power Systems," Energies, MDPI, vol. 13(18), pages 1-25, September.
    2. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.

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