IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3025-d560712.html
   My bibliography  Save this article

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

Author

Listed:
  • Minh-Quan Tran

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

  • Ahmed S. Zamzam

    (National Renewable Energy Laboratory, Golden, CO 80401, USA)

  • Phuong H. Nguyen

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

  • Guus Pemen

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

Abstract

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

Suggested Citation

  • Minh-Quan Tran & Ahmed S. Zamzam & Phuong H. Nguyen & Guus Pemen, 2021. "Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks," Energies, MDPI, vol. 14(11), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3025-:d:560712
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3025/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/11/3025/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vasileios Boglou & Christos-Spyridon Karavas & Konstantinos Arvanitis & Athanasios Karlis, 2020. "A Fuzzy Energy Management Strategy for the Coordination of Electric Vehicle Charging in Low Voltage Distribution Grids," Energies, MDPI, vol. 13(14), pages 1-34, July.
    2. Marzband, Mousa & Sumper, Andreas & Ruiz-Álvarez, Albert & Domínguez-García, José Luis & Tomoiagă, Bogdan, 2013. "Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets," Applied Energy, Elsevier, vol. 106(C), pages 365-376.
    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. Md Jakir Hossain & Mia Naeini, 2022. "Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters," Energies, MDPI, vol. 15(19), pages 1-17, September.
    2. Marco Pau & Paolo Attilio Pegoraro, 2022. "Monitoring and Automation of Complex Power Systems," Energies, MDPI, vol. 15(8), pages 1-3, April.

    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. Restrepo, Mauricio & Cañizares, Claudio A. & Simpson-Porco, John W. & Su, Peter & Taruc, John, 2021. "Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility," Applied Energy, Elsevier, vol. 290(C).
    2. A.S. Jameel Hassan & Umar Marikkar & G.W. Kasun Prabhath & Aranee Balachandran & W.G. Chaminda Bandara & Parakrama B. Ekanayake & Roshan I. Godaliyadda & Janaka B. Ekanayake, 2021. "A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration," Energies, MDPI, vol. 14(20), pages 1-24, October.
    3. Yossi Hadad & Baruch Keren & Dima Alberg, 2023. "An Expert System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements," Energies, MDPI, vol. 16(11), pages 1-18, May.
    4. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    5. Gayo-Abeleira, Miguel & Santos, Carlos & Javier Rodríguez Sánchez, Francisco & Martín, Pedro & Antonio Jiménez, José & Santiso, Enrique, 2022. "Aperiodic two-layer energy management system for community microgrids based on blockchain strategy," Applied Energy, Elsevier, vol. 324(C).
    6. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    7. Li, Bo & Li, Xu & Su, Qingyu, 2022. "A system and game strategy for the isolated island electric-gas deeply coupled energy network," Applied Energy, Elsevier, vol. 306(PA).
    8. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    9. Gonzalez de Durana, Jose & Barambones, Oscar, 2018. "Technology-free microgrid modeling with application to demand side management," Applied Energy, Elsevier, vol. 219(C), pages 165-178.
    10. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    11. Velik, Rosemarie & Nicolay, Pascal, 2014. "Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer," Applied Energy, Elsevier, vol. 130(C), pages 384-395.
    12. Roy, Kallol & Mandal, Kamal Krishna & Mandal, Atis Chandra, 2019. "Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system," Energy, Elsevier, vol. 167(C), pages 402-416.
    13. Obara, Shin’ya & Morizane, Yuta & Morel, Jorge, 2013. "Study on method of electricity and heat storage planning based on energy demand and tidal flow velocity forecasts for a tidal microgrid," Applied Energy, Elsevier, vol. 111(C), pages 358-373.
    14. Cheng-Shan Wang & Wei Li & Yi-Feng Wang & Fu-Qiang Han & Zhun Meng & Guo-Dong Li, 2017. "An Isolated Three-Port Bidirectional DC-DC Converter with Enlarged ZVS Region for HESS Applications in DC Microgrids," Energies, MDPI, vol. 10(4), pages 1-23, April.
    15. Arcos-Aviles, Diego & Pascual, Julio & Guinjoan, Francesc & Marroyo, Luis & Sanchis, Pablo & Marietta, Martin P., 2017. "Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting," Applied Energy, Elsevier, vol. 205(C), pages 69-84.
    16. Giampaolo Manzolini & Andrea Fusco & Domenico Gioffrè & Silvana Matrone & Riccardo Ramaschi & Marios Saleptsis & Riccardo Simonetti & Filip Sobic & Michael James Wood & Emanuele Ogliari & Sonia Leva, 2024. "Impact of PV and EV Forecasting in the Operation of a Microgrid," Forecasting, MDPI, vol. 6(3), pages 1-25, July.
    17. Meng Xiong & Feng Gao & Kun Liu & Siyun Chen & Jiaojiao Dong, 2015. "Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control," Energies, MDPI, vol. 8(8), pages 1-32, August.
    18. Xianyong Zhang & Yaohong Huang & Li Li & Wei-Chang Yeh, 2018. "Power and Capacity Consensus Tracking of Distributed Battery Storage Systems in Modular Microgrids," Energies, MDPI, vol. 11(6), pages 1-25, June.
    19. Toni Cantero Gubert & Alba Colet & Lluc Canals Casals & Cristina Corchero & José Luís Domínguez-García & Amelia Alvarez de Sotomayor & William Martin & Yves Stauffer & Pierre-Jean Alet, 2021. "Adaptive Volt-Var Control Algorithm to Grid Strength and PV Inverter Characteristics," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    20. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2019. "An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids," Renewable Energy, Elsevier, vol. 130(C), pages 1049-1066.

    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:14:y:2021:i:11:p:3025-:d:560712. 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.