IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v132y2017icp96-105.html
   My bibliography  Save this article

Multi-layer fuzzy-based under-frequency load shedding in back-pressure smart industrial microgrids

Author

Listed:
  • Khezri, Rahmat
  • Golshannavaz, Sajjad
  • Vakili, Ramin
  • Memar-Esfahani, Bahram

Abstract

In industrial microgrids (MGs), various operating conditions may lead to severe frequency fluctuations. If no any particular remedy is thought, these fluctuations may jeopardize the system stability against which load shedding is recognized as an effective countermeasure. This issue has not been properly addressed in back-pressure MGs where cogeneration of steam and electricity is required. If the permissible operating range is violated, under frequency load shedding (UFLS) scheme propagates suitable commands to disconnect a set of loads and hence assuring the frequency stability. This manuscript proposes an efficient multi-layer fuzzy-based load shedding (MLFLS) approach to enhance the MG overall performance. In comparison with conventional UFLS scheme, the amount of shed load is determined through a multi-layer mechanism. Frequency deviation and steam pressure are selected as two control inputs and the output signal is a control command to keep a specific load in connected or disconnected mode. To this end, suitable rule basis is embedded in MLFLS inference system to determine the amount of required load to be shed. The proposed approach is tested on a simulation model of a real-world autonomous MG and a suitable comparison is performed against the conventional UFLS scheme. The obtained results are discussed in depth.

Suggested Citation

  • Khezri, Rahmat & Golshannavaz, Sajjad & Vakili, Ramin & Memar-Esfahani, Bahram, 2017. "Multi-layer fuzzy-based under-frequency load shedding in back-pressure smart industrial microgrids," Energy, Elsevier, vol. 132(C), pages 96-105.
    • Handle: RePEc:eee:energy:v:132:y:2017:i:c:p:96-105
    DOI: 10.1016/j.energy.2017.05.059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217308149
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.05.059?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Akbari, Hashem & Warren, Mashuri & de Almeida, Anibal & Connell, Deborah & Harris, Jeffrey, 1988. "Use of energy management systems for performance monitoring of industrial load-shaping measures," Energy, Elsevier, vol. 13(3), pages 253-263.
    2. Malik, Arif S. & Bouzguenda, Mounir, 2013. "Effects of smart grid technologies on capacity and energy savings – A case study of Oman," Energy, Elsevier, vol. 54(C), pages 365-371.
    3. Jalali, Mohammad Majid & Kazemi, Ahad, 2015. "Demand side management in a smart grid with multiple electricity suppliers," Energy, Elsevier, vol. 81(C), pages 766-776.
    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. Skrjanc, T. & Mihalic, R. & Rudez, U., 2023. "A systematic literature review on under-frequency load shedding protection using clustering methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).
    2. Sohail Sarwar & Hazlie Mokhlis & Mohamadariff Othman & Munir Azam Muhammad & J. A. Laghari & Nurulafiqah Nadzirah Mansor & Hasmaini Mohamad & Alireza Pourdaryaei, 2020. "A Mixed Integer Linear Programming Based Load Shedding Technique for Improving the Sustainability of Islanded Distribution Systems," Sustainability, MDPI, vol. 12(15), pages 1-23, August.
    3. Ying-Yi Hong & Chih-Yang Hsiao, 2021. "Event-Based Under-Frequency Load Shedding Scheme in a Standalone Power System," Energies, MDPI, vol. 14(18), pages 1-19, September.

    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. Tengfei Ma & Junyong Wu & Liangliang Hao & Huaguang Yan & Dezhi Li, 2018. "A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach," Energies, MDPI, vol. 11(10), pages 1-19, October.
    2. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    3. Obu Samson Showers & Sunetra Chowdhury, 2024. "Enhancing Energy Supply Reliability for University Lecture Halls Using Photovoltaic-Battery Microgrids: A South African Case Study," Energies, MDPI, vol. 17(13), pages 1-26, June.
    4. Rauner, Sebastian & Eichhorn, Marcus & Thrän, Daniela, 2016. "The spatial dimension of the power system: Investigating hot spots of Smart Renewable Power Provision," Applied Energy, Elsevier, vol. 184(C), pages 1038-1050.
    5. Alshehri, Abdullah & Hussain, Ahmad & Mobarak, Youssef, 2014. "Energy-conversion measures in the industries of Saudi Arabia and development of methodology for certification of energy personnel in the Kingdom," Energy Policy, Elsevier, vol. 64(C), pages 203-208.
    6. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "Residential demand response scheme based on adaptive consumption level pricing," Energy, Elsevier, vol. 113(C), pages 301-308.
    7. Sarker, Eity & Seyedmahmoudian, Mehdi & Jamei, Elmira & Horan, Ben & Stojcevski, Alex, 2020. "Optimal management of home loads with renewable energy integration and demand response strategy," Energy, Elsevier, vol. 210(C).
    8. Mohammad Rozali, Nor Erniza & Wan Alwi, Sharifah Rafidah & Manan, Zainuddin Abdul & Klemeš, Jiří Jaromír, 2015. "Peak-off-peak load shifting for hybrid power systems based on Power Pinch Analysis," Energy, Elsevier, vol. 90(P1), pages 128-136.
    9. Koltsaklis, Nikolaos E. & Liu, Pei & Georgiadis, Michael C., 2015. "An integrated stochastic multi-regional long-term energy planning model incorporating autonomous power systems and demand response," Energy, Elsevier, vol. 82(C), pages 865-888.
    10. Personal, Enrique & Guerrero, Juan Ignacio & Garcia, Antonio & Peña, Manuel & Leon, Carlos, 2014. "Key performance indicators: A useful tool to assess Smart Grid goals," Energy, Elsevier, vol. 76(C), pages 976-988.
    11. Lee, Dasheng & Cheng, Chin-Chi, 2016. "Energy savings by energy management systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 760-777.
    12. Sadeghi, Hadi & Rashidinejad, Masoud & Abdollahi, Amir, 2017. "A comprehensive sequential review study through the generation expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1369-1394.
    13. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.
    14. Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.
    15. Gordon Rausser & Wadim Strielkowski & Dalia Å treimikienÄ—, 2018. "Smart meters and household electricity consumption: A case study in Ireland," Energy & Environment, , vol. 29(1), pages 131-146, February.
    16. Dong, Lianxin & Fan, Shuai & Wang, Zhihua & Xiao, Jucheng & Zhou, Huan & Li, Zuyi & He, Guangyu, 2021. "An adaptive decentralized economic dispatch method for virtual power plant," Applied Energy, Elsevier, vol. 300(C).
    17. Fotopoulou, Maria & Rakopoulos, Dimitrios & Petridis, Stefanos & Drosatos, Panagiotis, 2024. "Assessment of smart grid operation under emergency situations," Energy, Elsevier, vol. 287(C).
    18. Rabiee, Abdorreza & Sadeghi, Mohammad & Aghaeic, Jamshid & Heidari, Alireza, 2016. "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 721-739.
    19. Farzamkia, Saleh & Ranjbar, Hossein & Hatami, Alireza & Iman-Eini, Hossein, 2016. "A novel PSO (Particle Swarm Optimization)-based approach for optimal schedule of refrigerators using experimental models," Energy, Elsevier, vol. 107(C), pages 707-715.
    20. Zhang, Hongwei & Wang, Jinsong & Ding, Yuemin, 2019. "Blockchain-based decentralized and secure keyless signature scheme for smart grid," Energy, Elsevier, vol. 180(C), pages 955-967.

    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:eee:energy:v:132:y:2017:i:c:p:96-105. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.