IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v269y2020ics0306261920305894.html
   My bibliography  Save this article

Optimal demand response operation of electric boosting glass furnaces

Author

Listed:
  • Seo, Kyeongjun
  • Edgar, Thomas F.
  • Baldea, Michael

Abstract

The glass industry is highly energy-intensive, accounting for 1% of total industrial energy consumption in the United States. Most of the energy consumption in the glass manufacturing process is attributable to the significant heat required to melt raw materials. An electric boosting system which can transfer extra heat (5%–20% of total energy) to the glass melt in addition to the energy from natural gas combustion can be implemented in a glass furnace. Electric boosting is thermally efficient, reduces direct pollutant emissions, and prolongs furnace superstructure lifespan. However, a high level of electric boost is not always economically desirable, considering the volatility of electricity prices. Balancing between natural gas and electricity consumption in a demand response strategy can reduce the energy cost and mitigate strain on the electrical grid. In this paper, a physics-based model is developed to describe the dynamic behavior of a prototype electric boosting glass furnace. We present a dynamic optimization strategy to optimally balance between using natural gas and electric power under electricity price fluctuations. Case studies on the effect of varying energy prices and emissions regulations are analyzed.

Suggested Citation

  • Seo, Kyeongjun & Edgar, Thomas F. & Baldea, Michael, 2020. "Optimal demand response operation of electric boosting glass furnaces," Applied Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920305894
    DOI: 10.1016/j.apenergy.2020.115077
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920305894
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115077?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. Otashu, Joannah I. & Baldea, Michael, 2018. "Grid-level “battery” operation of chemical processes and demand-side participation in short-term electricity markets," Applied Energy, Elsevier, vol. 220(C), pages 562-575.
    2. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    3. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2016. "The behaviour mechanism analysis of regional natural gas prices: A multi-scale perspective," Energy, Elsevier, vol. 101(C), pages 266-277.
    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. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    2. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

    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. 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.
    2. Behnam Zakeri & Samuli Rinne & Sanna Syri, 2015. "Wind Integration into Energy Systems with a High Share of Nuclear Power—What Are the Compromises?," Energies, MDPI, vol. 8(4), pages 1-35, March.
    3. Jin, Hongyang & Li, Zhengshuo & Sun, Hongbin & Guo, Qinglai & Chen, Runze & Wang, Bin, 2017. "A robust aggregate model and the two-stage solution method to incorporate energy intensive enterprises in power system unit commitment," Applied Energy, Elsevier, vol. 206(C), pages 1364-1378.
    4. Murphy, M.D. & O’Mahony, M.J. & Upton, J., 2015. "Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment," Applied Energy, Elsevier, vol. 149(C), pages 392-403.
    5. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    6. Cuilin Li & Ya-Juan Du & Qiang Ji & Jiang-bo Geng, 2019. "Multiscale Market Integration and Nonlinear Granger Causality between Natural Gas Futures and Physical Markets," Sustainability, MDPI, vol. 11(19), pages 1-23, October.
    7. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    8. Sivaneasan, Balakrishnan & Kandasamy, Nandha Kumar & Lim, May Lin & Goh, Kwang Ping, 2018. "A new demand response algorithm for solar PV intermittency management," Applied Energy, Elsevier, vol. 218(C), pages 36-45.
    9. Gallo, A.B. & Simões-Moreira, J.R. & Costa, H.K.M. & Santos, M.M. & Moutinho dos Santos, E., 2016. "Energy storage in the energy transition context: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 800-822.
    10. Chai, Jian & Zhang, Xiaokong & Lu, Quanying & Zhang, Xuejun & Wang, Yabo, 2021. "Research on imbalance between supply and demand in China's natural gas market under the double-track price system," Energy Policy, Elsevier, vol. 155(C).
    11. Anamarija Falkoni & Antun Pfeifer & Goran Krajačić, 2020. "Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs," Energies, MDPI, vol. 13(6), pages 1-22, March.
    12. Anees, Amir & Chen, Yi-Ping Phoebe, 2016. "True real time pricing and combined power scheduling of electric appliances in residential energy management system," Applied Energy, Elsevier, vol. 165(C), pages 592-600.
    13. Liu, Kun & Guan, Xiaohong & Gao, Feng & Zhai, Qiaozhu & Wu, Jiang, 2015. "Self-balancing robust scheduling with flexible batch loads for energy intensive corporate microgrid," Applied Energy, Elsevier, vol. 159(C), pages 391-400.
    14. Carreiro, Andreia M. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2017. "Energy management systems aggregators: A literature survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1160-1172.
    15. Coninx, Kristof & Deconinck, Geert & Holvoet, Tom, 2018. "Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics," Applied Energy, Elsevier, vol. 218(C), pages 104-113.
    16. Andre Leippi & Markus Fleschutz & Michael D. Murphy, 2022. "A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios," Energies, MDPI, vol. 15(9), pages 1-22, April.
    17. Ranaboldo, M. & Aragüés-Peñalba, M. & Arica, E. & Bade, A. & Bullich-Massagué, E. & Burgio, A. & Caccamo, C. & Caprara, A. & Cimmino, D. & Domenech, B. & Donoso, I. & Fragapane, G. & González-Font-de-, 2024. "A comprehensive overview of industrial demand response status in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    18. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
    19. Li, Xiuming & Sun, Mei & Gao, Cuixia & He, Huizi, 2019. "The spillover effects between natural gas and crude oil markets: The correlation network analysis based on multi-scale approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 306-324.
    20. Xie Jiaping & Zhang Weisi & Xia Yu & Liang Ling & Kong Lingcheng, 2018. "Electricity Price of Hybrid Power System and Decision Making of Renewable Energy Investment Capacity," Journal of Systems Science and Information, De Gruyter, vol. 6(3), pages 193-213, June.

    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:appene:v:269:y:2020:i:c:s0306261920305894. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.