IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v86y2009i7-8p1266-1273.html
   My bibliography  Save this article

An optimal control model for load shifting - With application in the energy management of a colliery

Author

Listed:
  • Middelberg, Arno
  • Zhang, Jiangfeng
  • Xia, Xiaohua

Abstract

This paper presents an optimal control model for the load shifting problem in energy management and its application in a South African colliery. It is illustrated in the colliery scenario that how the optimal control model can be applied to optimize load shifting and improve energy efficiency through the control of conveyor belts. The time-of-use electricity tariff is used as an input to the objective function in order to obtain a solution that minimizes electricity costs and thus maximizes load shifting. The case study yields promising results that show the potential of applying this optimal control model to other industrial Demand Side Management initiatives.

Suggested Citation

  • Middelberg, Arno & Zhang, Jiangfeng & Xia, Xiaohua, 2009. "An optimal control model for load shifting - With application in the energy management of a colliery," Applied Energy, Elsevier, vol. 86(7-8), pages 1266-1273, July.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:7-8:p:1266-1273
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(08)00233-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Chaabene, Maher & Ammar, Mohsen Ben & Elhajjaji, Ahmed, 2007. "Fuzzy approach for optimal energy-management of a domestic photovoltaic panel," Applied Energy, Elsevier, vol. 84(10), pages 992-1001, October.
    2. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    Full references (including those not matched with items on IDEAS)

    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. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    2. Mao Tan & Bin Duan & Yongxin Su, 2018. "Economic batch sizing and scheduling on parallel machines under time-of-use electricity pricing," Operational Research, Springer, vol. 18(1), pages 105-122, April.
    3. Alsaedi, Yasir Hamad & Tularam, Gurudeo Anand, 2020. "The relationship between electricity consumption, peak load and GDP in Saudi Arabia: A VAR analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 175(C), pages 164-178.
    4. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    5. 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.
    6. Dashti, Reza & Afsharnia, Saeed & Ghasemi, Hassan, 2010. "A new long term load management model for asset governance of electrical distribution systems," Applied Energy, Elsevier, vol. 87(12), pages 3661-3667, December.
    7. Yang, Jiaojiao & Sun, Zeyi & Hu, Wenqing & Steinmeister, Louis, 2022. "Joint control of manufacturing and onsite microgrid system via novel neural-network integrated reinforcement learning algorithms," Applied Energy, Elsevier, vol. 315(C).
    8. Rajeev, T. & Ashok, S., 2015. "Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources," Applied Energy, Elsevier, vol. 146(C), pages 141-149.
    9. 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.
    10. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    11. Gulin, Marko & Vašak, Mario & Perić, Nedjeljko, 2013. "Dynamical optimal positioning of a photovoltaic panel in all weather conditions," Applied Energy, Elsevier, vol. 108(C), pages 429-438.
    12. Qiao, Zheng & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Liu, Yuquan & Xiong, Wen, 2017. "An interval gas flow analysis in natural gas and electricity coupled networks considering the uncertainty of wind power," Applied Energy, Elsevier, vol. 201(C), pages 343-353.
    13. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    14. van Staden, Adam Jacobus & Zhang, Jiangfeng & Xia, Xiaohua, 2011. "A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges," Applied Energy, Elsevier, vol. 88(12), pages 4785-4794.
    15. Gahm, Christian & Denz, Florian & Dirr, Martin & Tuma, Axel, 2016. "Energy-efficient scheduling in manufacturing companies: A review and research framework," European Journal of Operational Research, Elsevier, vol. 248(3), pages 744-757.
    16. Dowling, Alexander W. & Kumar, Ranjeet & Zavala, Victor M., 2017. "A multi-scale optimization framework for electricity market participation," Applied Energy, Elsevier, vol. 190(C), pages 147-164.
    17. Cavallaro, Fausto, 2010. "Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems," Applied Energy, Elsevier, vol. 87(2), pages 496-503, February.
    18. Wang, Jiayang & Wang, Qiang & Sun, Wenqiang, 2023. "Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry," Applied Energy, Elsevier, vol. 342(C).
    19. Zhuan, Xiangtao & Xia, Xiaohua, 2013. "Optimal operation scheduling of a pumping station with multiple pumps," Applied Energy, Elsevier, vol. 104(C), pages 250-257.
    20. Fernandez, Mayela & Li, Lin & Sun, Zeyi, 2013. "“Just-for-Peak” buffer inventory for peak electricity demand reduction of manufacturing systems," International Journal of Production Economics, Elsevier, vol. 146(1), pages 178-184.

    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:86:y:2009:i:7-8:p:1266-1273. 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.