IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4812-d413565.html
   My bibliography  Save this article

Technical Measures to Mitigate Load Fluctuation for Large-Scale Customers to Improve Power System Energy Efficiency

Author

Listed:
  • Yongsik Lee

    (Director of Research, SRENG Corp., Suncheon 58023, Korea)

  • Hyunchul Lee

    (Department of Electrical Engineering, Woosuk University, Wanju 55338, Korea)

  • Jaehyeon Gim

    (Department of Electrical Engineering, Sunchon National University, Suncheon 57922, Korea)

  • Inyong Seo

    (College of Information and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Guenjoon Lee

    (Department of Electric Energy Systems, Chungbuk Provincial University, Okcheon 29046, Korea)

Abstract

Industrial equipment such as electric arc furnaces and steel mills are often associated with rapid and high load disturbances, so their power systems require additional control equipment to limit the frequency. However, proper ancillary service fees are not paid in these cases with extreme and variable load demands. The frequency regulation reserve equipment adds to power generation costs. Therefore, variable power generation loads lead to increases in the cost of energy production. We propose a load frequency control method that is applied on the customer end instead of the power supply end to reduce the operating reserve required to improve the energy efficiency of the power system. We analyzed the load fluctuation of steel mill customers using real data sampled at two-second intervals from the energy management system in Korea. We developed an automatic generation control program to simulate the power system’s frequency characteristics. We also propose compensation techniques for mitigation of the system’s frequency deviation at the customer end based on an energy storage system, pump storage hydro generator, customer generator, and plant process adjustment. To recover the frequency deviation, we calculated the compensation facility capacity and analyzed static characteristics, and we proved the feasibility via simulations.

Suggested Citation

  • Yongsik Lee & Hyunchul Lee & Jaehyeon Gim & Inyong Seo & Guenjoon Lee, 2020. "Technical Measures to Mitigate Load Fluctuation for Large-Scale Customers to Improve Power System Energy Efficiency," Energies, MDPI, vol. 13(18), pages 1-27, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4812-:d:413565
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4812/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4812/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jie Song & Xin Pan & Chao Lu & Hanchen Xu, 2017. "A Simulation-Based Optimization Method for Hybrid Frequency Regulation System Configuration," Energies, MDPI, vol. 10(9), pages 1-14, August.
    2. Dehghanpour, Kaveh & Afsharnia, Saeed, 2015. "Electrical demand side contribution to frequency control in power systems: a review on technical aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1267-1276.
    3. Jingyi Zhang & Chao Lu & Jie Song, 2016. "Dynamic performance-based automatic generation control unit allocation with frequency sensitivity identification," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6532-6547, November.
    4. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
    5. Joanna Nowicka-Zagrajek & Rafal Weron, 2002. "Modeling electricity loads in California: ARMA models with hyperbolic noise," HSC Research Reports HSC/02/02, Hugo Steinhaus Center, Wroclaw University of Technology.
    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. SungHoon Lim & Taewan Kim & Kipo Yoon & DongHee Choi & Jung-Wook Park, 2022. "A Study on Frequency Stability and Primary Frequency Response of the Korean Electric Power System Considering the High Penetration of Wind Power," Energies, MDPI, vol. 15(5), pages 1-16, February.

    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. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    2. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    3. Farahmand, H. & Doorman, G.L., 2012. "Balancing market integration in the Northern European continent," Applied Energy, Elsevier, vol. 96(C), pages 316-326.
    4. Dreidy, Mohammad & Mokhlis, H. & Mekhilef, Saad, 2017. "Inertia response and frequency control techniques for renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 144-155.
    5. Michael Schoepf & Martin Weibelzahl & Lisa Nowka, 2018. "The Impact of Substituting Production Technologies on the Economic Demand Response Potential in Industrial Processes," Energies, MDPI, vol. 11(9), pages 1-13, August.
    6. Neda Hajibandeh & Mehdi Ehsan & Soodabeh Soleymani & Miadreza Shafie-khah & João P. S. Catalão, 2017. "The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey," Energies, MDPI, vol. 10(9), pages 1-18, September.
    7. Amaral, Luiz Felipe & Souza, Reinaldo Castro & Stevenson, Maxwell, 2008. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 603-615.
    8. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    9. Rusche, Simon & Weissflog., Jan & Wenninger, Simon & Häckel, Björn, 2023. "How flexible are energy flexibilities? Developing a flexibility score for revenue and risk analysis in industrial demand-side management," Applied Energy, Elsevier, vol. 345(C).
    10. Hirase, Yuko & Abe, Kensho & Sugimoto, Kazushige & Sakimoto, Kenichi & Bevrani, Hassan & Ise, Toshifumi, 2018. "A novel control approach for virtual synchronous generators to suppress frequency and voltage fluctuations in microgrids," Applied Energy, Elsevier, vol. 210(C), pages 699-710.
    11. Tang, Yi & Li, Feng & Chen, Qian & Li, Mengya & Wang, Qi & Ni, Ming & Chen, Gang, 2018. "Frequency prediction method considering demand response aggregate characteristics and control effects," Applied Energy, Elsevier, vol. 229(C), pages 936-944.
    12. Bey, M. & Hamidat, A. & Benyoucef, B. & Nacer, T., 2016. "Viability study of the use of grid connected photovoltaic system in agriculture: Case of Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 333-345.
    13. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    14. Paterakis, Nikolaos G. & Erdinç, Ozan & Catalão, João P.S., 2017. "An overview of Demand Response: Key-elements and international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 871-891.
    15. Richstein, Jörn C. & Hosseinioun, Seyed Saeed, 2020. "Industrial demand response: How network tariffs and regulation (do not) impact flexibility provision in electricity markets and reserves," Applied Energy, Elsevier, vol. 278(C).
    16. 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.
    17. Vanessa Zawodnik & Florian Christian Schwaiger & Christoph Sorger & Thomas Kienberger, 2024. "Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters," Energies, MDPI, vol. 17(6), pages 1-20, March.
    18. Ambrosius, Mirjam & Grimm, Veronika & Sölch, Christian & Zöttl, Gregor, 2018. "Investment incentives for flexible demand options under different market designs," Energy Policy, Elsevier, vol. 118(C), pages 372-389.
    19. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
    20. Nouha Mansouri & Abderezak Lashab & Dezso Sera & Josep M. Guerrero & Adnen Cherif, 2019. "Large Photovoltaic Power Plants Integration: A Review of Challenges and Solutions," Energies, MDPI, vol. 12(19), pages 1-16, October.

    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:13:y:2020:i:18:p:4812-:d:413565. 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.