IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i12p3333-d186449.html
   My bibliography  Save this article

FLISR Approach for Smart Distribution Networks Using E-Terra Software—A Case Study

Author

Listed:
  • Duy Phuc Le

    (Load Dispatching Center, Ho Chi Minh Power Corporation, Ho Chi Minh City 70000, Vietnam
    Institute of Engineering, Ho Chi Minh University of Technology (HUTECH), Ho Chi Minh City 70000, Vietnam)

  • Duong Minh Bui

    (Institute of Engineering, Ho Chi Minh University of Technology (HUTECH), Ho Chi Minh City 70000, Vietnam
    Faculty of Engineering, Vietnamese-German University (VGU), Thu Dau Mot City 59000, Binh Duong Province, Vietnam)

  • Cao Cuong Ngo

    (Institute of Engineering, Ho Chi Minh University of Technology (HUTECH), Ho Chi Minh City 70000, Vietnam)

  • Anh My Thi Le

    (Faculty of Engineering, Vietnamese-German University (VGU), Thu Dau Mot City 59000, Binh Duong Province, Vietnam)

Abstract

A smart grid concept has been defined in recent years, which emphasizes the importance on smart protection and measurement devices, reliable data communication and high security, optimal energy management system, and fault detection, location, isolation and service restoration (FLISR) of distribution networks (DNs). The main objectives of the FLISR approach are to achieve fast fault processing time, reduce the minimum number of interrupted customers, and improve the power supply reliability of the distribution. The conventional FLISR approach is to use signals of fault indicators (FIs) with distribution network states. The discrete installation of FIs to switches or reclosers may slow the processing time of fault detection and location, so it is necessary to develop a more efficient FLISR approach for smart distribution networks using functions of feeder terminal units (FTUs). In this paper, pick-up and tripping signals of overcurrent (OC) relays in combination with distribution grid states (e.g., switching status of devices, loss of voltage…) sent from feeder terminal units (FTUs) are used to detect and locate different fault types. Fault isolation and service restoration of black-out areas are then performed by solving an objective function with two main constraints, including (i) restoring the possible maximum number of out-of-service loads; and (ii) limiting the minimum number of switching operation. Thirteen performance factors (PF) are used for the post-fault service restoration process, consisting of: (i) Power Flow Violations (PFV), (ii) Bus Voltage Violations (BVV), (iii) Total Operation Cost (TOC), (iv) Lost Power (LP), (v) Outage Customer (OC), (vi) Number of Switching Steps (NSS), (vii) Power Losses (LOSS); (viii) Customer Minutes Interruption (CMI), (ix) Load Minutes Interruption (LMI), (x) MAIFI, (xi) SAIFI, (xii) SAIDI, and (xiii) Protection Validation (PRV). E-Terra platform of a distribution management system (DMS) is used to implement the proposed FLISR approach. Simulation and experiment results from a real 22 kV distribution network are also analysed to validate this FLISR approach. As a result, the novel FLISR approach has the ability to identify effectively the over-reaching of OC relays, indicate a mis-coordination risk of adjacent protection devices on the same feeder, and get the total processing time of fault detection, location and isolation as well as ranking all possible service restoration plans in distribution network at less than two minutes.

Suggested Citation

  • Duy Phuc Le & Duong Minh Bui & Cao Cuong Ngo & Anh My Thi Le, 2018. "FLISR Approach for Smart Distribution Networks Using E-Terra Software—A Case Study," Energies, MDPI, vol. 11(12), pages 1-33, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3333-:d:186449
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/12/3333/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/12/3333/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Incorporating load variation and variable wind generation in service restoration plans for distribution systems," Energy, Elsevier, vol. 57(C), pages 682-691.
    2. Thomas Taylor & Peter Schwarz & James Cochell, 2005. "24/7 Hourly Response to Electricity Real-Time Pricing with up to Eight Summers of Experience," Journal of Regulatory Economics, Springer, vol. 27(3), pages 235-262, January.
    3. Zarnikau, Jay W., 2010. "Demand participation in the restructured Electric Reliability Council of Texas market," Energy, Elsevier, vol. 35(4), pages 1536-1543.
    4. Herter, Karen, 2007. "Residential implementation of critical-peak pricing of electricity," Energy Policy, Elsevier, vol. 35(4), pages 2121-2130, April.
    5. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    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. Shaheen Kousar & Nazir Ahmad Zafar & Tariq Ali & Eman H. Alkhammash & Myriam Hadjouni, 2022. "Formal Modeling of IoT-Based Distribution Management System for Smart Grids," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    2. Davide Della Giustina & Amelia Alvarez de Sotomayor & Alessio Dedè & Francisco Ramos, 2020. "A Model-Based Design of Distributed Automation Systems for the Smart Grid: Implementation and Validation," Energies, MDPI, vol. 13(14), pages 1-19, July.
    3. Ednardo Rocha & Max Pimentel Filho & Melinda Cruz & Marcos Almeida & Manoel Medeiros Júnior, 2020. "A New Linear State Estimator for Fault Location in Distribution Systems Based on Backward-Forward Currents Sweep," Energies, MDPI, vol. 13(11), pages 1-23, May.
    4. Mirosław Kornatka & Tomasz Popławski, 2021. "Advanced Metering Infrastructure—Towards a Reliable Network," Energies, MDPI, vol. 14(18), pages 1-12, September.
    5. Peter Holcsik & Judith Pálfi & Miklos Tompa & Janusz Grabara & Zsolt Čonka & Michal Kolcun & Mihai Avornicului & Karol Jędrasiak, 2020. "Management of Smart Switchboard Placement to Enhance Distribution System Reliability," Energies, MDPI, vol. 13(6), pages 1-13, March.

    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. Jimyung Kang & Jee-Hyong Lee, 2017. "Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers," Energies, MDPI, vol. 10(10), pages 1-17, October.
    2. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    3. Nikzad, Mehdi & Mozafari, Babak & Bashirvand, Mahdi & Solaymani, Soodabeh & Ranjbar, Ali Mohamad, 2012. "Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index," Energy, Elsevier, vol. 41(1), pages 541-548.
    4. Behrangrad, Mahdi & Sugihara, Hideharu & Funaki, Tsuyoshi, 2011. "Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market," Applied Energy, Elsevier, vol. 88(7), pages 2548-2558, July.
    5. Botelho, D.F. & de Oliveira, L.W. & Dias, B.H. & Soares, T.A. & Moraes, C.A., 2022. "Prosumer integration into the Brazilian energy sector: An overview of innovative business models and regulatory challenges," Energy Policy, Elsevier, vol. 161(C).
    6. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    7. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    8. Jimyung Kang & Soonwoo Lee, 2018. "Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System," Energies, MDPI, vol. 11(11), pages 1-14, October.
    9. Kakran, Sandeep & Chanana, Saurabh, 2018. "Smart operations of smart grids integrated with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 524-535.
    10. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    11. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    12. Bae, Mungyu & Kim, Hwantae & Kim, Eugene & Chung, Albert Yongjoon & Kim, Hwangnam & Roh, Jae Hyung, 2014. "Toward electricity retail competition: Survey and case study on technical infrastructure for advanced electricity market system," Applied Energy, Elsevier, vol. 133(C), pages 252-273.
    13. Makena Coffman & Paul Bernstein & Sherilyn Wee & Aida Arik, 2016. "Estimating the Opportunity for Load-Shifting in Hawaii: An Analysis of Proposed Residential Time-of-Use Rates," Working Papers 2016-10, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    14. Park, S.C. & Jin, Y.G. & Song, H.Y. & Yoon, Y.T., 2015. "Designing a critical peak pricing scheme for the profit maximization objective considering price responsiveness of customers," Energy, Elsevier, vol. 83(C), pages 521-531.
    15. He, Yongxiu & Wang, Bing & Wang, Jianhui & Xiong, Wei & Xia, Tian, 2012. "Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China," Energy, Elsevier, vol. 47(1), pages 230-236.
    16. Kazutoshi Tsuda & Michinori Uwasu & Keishiro Hara & Yukari Fuchigami, 2017. "Approaches to induce behavioral changes with respect to electricity consumption," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 7(1), pages 30-38, March.
    17. Lee, Kyungeun & Lee, Hyesu & Lee, Hyoseop & Yoon, Yoonjin & Lee, Eunjung & Rhee, Wonjong, 2018. "Assuring explainability on demand response targeting via credit scoring," Energy, Elsevier, vol. 161(C), pages 670-679.
    18. Moore, J. & Woo, C.K. & Horii, B. & Price, S. & Olson, A., 2010. "Estimating the option value of a non-firm electricity tariff," Energy, Elsevier, vol. 35(4), pages 1609-1614.
    19. Yiqi Dong & Zuoji Dong, 2023. "Bibliometric Analysis of Game Theory on Energy and Natural Resource," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    20. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.

    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:11:y:2018:i:12:p:3333-:d:186449. 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.