IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i11p2925-d178578.html
   My bibliography  Save this article

A Practical Load-Source Coordinative Method for Further Reducing Curtailed Wind Power in China with Energy-Intensive Loads

Author

Listed:
  • Dandan Zhu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Wenying Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Yang Hu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Weizhou Wang

    (State Grid Corporation of Gansu Province, Lanzhou 730000, China)

Abstract

Large-scale wind power farms in China are suffering from large amounts of curtailed wind power in the conventional dispatching mode. Energy-intensive loads have great potential in providing service regulation to help consume the curtailed wind power. However, constrained by technical limitations, the regulating power of energy-intensive loads cannot fully follow the fluctuating wind power. This would result in insufficient utilization of the regulation capability of energy-intensive loads, which limits the promotion in consumption of curtailed wind power brought by load-source coordination. With this concern, a brand new method for further consuming the curtailed wind power, which involves using coal-fueled units to provide ancillary regulation for the coordination of energy-intensive loads and wind power is presented, and a corresponding bi-level coordinative model is also established. The first level of optimization is intended to maximize the consumption of curtailed wind power with minimum ancillary regulation energy. The second level of optimization is to allocate the ancillary regulating power with minimum regulation cost. Wind power consumption was increased by 3,369.25 MWh and utilization rate of energy-intensive loads was promoted to 100% in the case analysis, which verifies the effectiveness of the proposed method.

Suggested Citation

  • Dandan Zhu & Wenying Liu & Yang Hu & Weizhou Wang, 2018. "A Practical Load-Source Coordinative Method for Further Reducing Curtailed Wind Power in China with Energy-Intensive Loads," Energies, MDPI, vol. 11(11), pages 1-14, October.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2925-:d:178578
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/11/2925/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/11/2925/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Runze & Sun, Hongbin & Guo, Qinglai & Jin, Hongyang & Wu, Wenchuan & Zhang, Boming, 2015. "Profit-seeking energy-intensive enterprises participating in power system scheduling: Model and mechanism," Applied Energy, Elsevier, vol. 158(C), pages 263-274.
    2. Ashok, S. & Banerjee, R., 2000. "Load-management applications for the industrial sector," Applied Energy, Elsevier, vol. 66(2), pages 105-111, June.
    3. Ramin, D. & Spinelli, S. & Brusaferri, A., 2018. "Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process," Applied Energy, Elsevier, vol. 225(C), pages 622-636.
    4. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    5. 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.
    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. Yuwei Zhang & Wenying Liu & Yue Huan & Qiang Zhou & Ningbo Wang, 2020. "An Optimal Day-Ahead Thermal Generation Scheduling Method to Enhance Total Transfer Capability for the Sending-Side System with Large-Scale Wind Power Integration," Energies, MDPI, vol. 13(9), pages 1-19, May.

    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. Loganthurai, P. & Rajasekaran, V. & Gnanambal, K., 2016. "Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand," Energy, Elsevier, vol. 107(C), pages 419-430.
    2. 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).
    3. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    4. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
    5. Wanapinit, Natapon & Thomsen, Jessica & Kost, Christoph & Weidlich, Anke, 2021. "An MILP model for evaluating the optimal operation and flexibility potential of end-users," Applied Energy, Elsevier, vol. 282(PB).
    6. 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).
    7. Wanapinit, Natapon & Thomsen, Jessica & Weidlich, Anke, 2022. "Integrating flexibility provision into operation planning: A generic framework to assess potentials and bid prices of end-users," Energy, Elsevier, vol. 261(PB).
    8. 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.
    9. 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.
    10. Finn, P. & O’Connell, M. & Fitzpatrick, C., 2013. "Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction," Applied Energy, Elsevier, vol. 101(C), pages 678-685.
    11. 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.
    12. Liao, Siyang & Xu, Jian & Sun, Yuanzhang & Bao, Yi, 2018. "Local utilization of wind electricity in isolated power systems by employing coordinated control scheme of industrial energy-intensive load," Applied Energy, Elsevier, vol. 217(C), pages 14-24.
    13. 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).
    14. 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).
    15. 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.
    16. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    17. 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.
    18. Summerbell, Daniel L. & Khripko, Diana & Barlow, Claire & Hesselbach, Jens, 2017. "Cost and carbon reductions from industrial demand-side management: Study of potential savings at a cement plant," Applied Energy, Elsevier, vol. 197(C), pages 100-113.
    19. 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.
    20. Kwon, Pil Seok & Østergaard, Poul, 2014. "Assessment and evaluation of flexible demand in a Danish future energy scenario," Applied Energy, Elsevier, vol. 134(C), pages 309-320.

    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:11:p:2925-:d:178578. 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.