IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1295-d219955.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1295/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/7/1295/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ayman Esmat & Julio Usaola & María Ángeles Moreno, 2018. "Distribution-Level Flexibility Market for Congestion Management," Energies, MDPI, vol. 11(5), pages 1-24, April.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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. 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.

    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. Motalleb, Mahdi & Thornton, Matsu & Reihani, Ehsan & Ghorbani, Reza, 2016. "A nascent market for contingency reserve services using demand response," Applied Energy, Elsevier, vol. 179(C), pages 985-995.
    2. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    3. Bhagya Nathali Silva & Murad Khan & Kijun Han, 2020. "Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities," Sustainability, MDPI, vol. 12(14), pages 1-23, July.
    4. Jing Wu & Kun Li & Jing Sun & Li Xie, 2018. "A Novel Integrated Method to Diagnose Faults in Power Transformers," Energies, MDPI, vol. 11(11), pages 1-8, November.
    5. Schreiner, Lena & Madlener, Reinhard, 2022. "Investing in power grid infrastructure as a flexibility option: A DSGE assessment for Germany," Energy Economics, Elsevier, vol. 107(C).
    6. Mehran Tahir & Stefan Tenbohlen, 2019. "A Comprehensive Analysis of Windings Electrical and Mechanical Faults Using a High-Frequency Model," Energies, MDPI, vol. 13(1), pages 1-25, December.
    7. Benoît Bletterie & Serdar Kadam & Herwig Renner, 2018. "On the Classification of Low Voltage Feeders for Network Planning and Hosting Capacity Studies," Energies, MDPI, vol. 11(3), pages 1-23, March.
    8. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    9. Lefeng Cheng & Tao Yu & Guoping Wang & Bo Yang & Lv Zhou, 2018. "Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study," Energies, MDPI, vol. 11(1), pages 1-26, January.
    10. Anilkumar, T.T. & Simon, Sishaj P. & Padhy, Narayana Prasad, 2017. "Residential electricity cost minimization model through open well-pico turbine pumped storage system," Applied Energy, Elsevier, vol. 195(C), pages 23-35.
    11. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    12. Jiefeng Liu & Hanbo Zheng & Yiyi Zhang & Hua Wei & Ruijin Liao, 2017. "Grey Relational Analysis for Insulation Condition Assessment of Power Transformers Based Upon Conventional Dielectric Response Measurement," Energies, MDPI, vol. 10(10), pages 1-16, October.
    13. Bossink, Bart A.G., 2017. "Demonstrating sustainable energy: A review based model of sustainable energy demonstration projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1349-1362.
    14. MHD Nour Hindia & Faizan Qamar & Mohammad B. Majed & Tharek Abd Rahman & Iraj S. Amiri, 2019. "Enabling remote-control for the power sub-stations over LTE-A networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(1), pages 37-53, January.
    15. Sadeghianpourhamami, N. & Demeester, T. & Benoit, D.F. & Strobbe, M. & Develder, C., 2016. "Modeling and analysis of residential flexibility: Timing of white good usage," Applied Energy, Elsevier, vol. 179(C), pages 790-805.
    16. Fraga, Carolina & Hollmuller, Pierre & Schneider, Stefan & Lachal, Bernard, 2018. "Heat pump systems for multifamily buildings: Potential and constraints of several heat sources for diverse building demands," Applied Energy, Elsevier, vol. 225(C), pages 1033-1053.
    17. Martínez-Lao, Juan & Montoya, Francisco G. & Montoya, Maria G. & Manzano-Agugliaro, Francisco, 2017. "Electric vehicles in Spain: An overview of charging systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 970-983.
    18. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
    19. Yulong Wang & Xiaohong Zhang & Lili Li & Jinyang Du & Junguo Gao, 2019. "Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring," Energies, MDPI, vol. 12(24), pages 1-14, December.
    20. Choi, Kwang Hun & Kwon, Gyu Hyun, 2023. "Strategies for sensing innovation opportunities in smart grids: In the perspective of interactive relationships between science, technology, and business," Technological Forecasting and Social Change, Elsevier, vol. 187(C).

    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:12:y:2019:i:7:p:1295-:d:219955. 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.