IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v281y2020i1p186-200.html
   My bibliography  Save this article

Multi-process production scheduling with variable renewable integration and demand response

Author

Listed:
  • Ruiz Duarte, José Luis
  • Fan, Neng
  • Jin, Tongdan

Abstract

Integrating renewable energy sources to power manufacturing facilities is one approach to achieve low carbon economy. The contribution of this paper is to propose a way to facilitate and assess renewable sources’ integration into manufacturing systems, by exploring an optimization model that obtains a production schedule adapted to match the onsite renewable energy supply, with energy storage systems and the power grid as backups. A multi-process production scheme as well as demand side management policies such as Time-and-Level-of-Use and power consumption reduction requests are considered. To capture renewable uncertainties, a two-stage robust optimization model is formulated to optimize the production scheduling under the worst-case scenario of renewable generation. A nested Column-and-Constraint Generation algorithm is applied to solve this formulation. Numerical experiments are performed on a benchmark case, and sensitivity analysis is conducted by modifying renewable integration, uncertainty, data granularity, scheduling horizon, switch of on-peak prices hours, and zero-inventory policy. Obtained results validate the proposed model and algorithm.

Suggested Citation

  • Ruiz Duarte, José Luis & Fan, Neng & Jin, Tongdan, 2020. "Multi-process production scheduling with variable renewable integration and demand response," European Journal of Operational Research, Elsevier, vol. 281(1), pages 186-200.
  • Handle: RePEc:eee:ejores:v:281:y:2020:i:1:p:186-200
    DOI: 10.1016/j.ejor.2019.08.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221719306708
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2019.08.017?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Roksana Yasmin & B. M. Ruhul Amin & Rakibuzzaman Shah & Andrew Barton, 2024. "A Survey of Commercial and Industrial Demand Response Flexibility with Energy Storage Systems and Renewable Energy," Sustainability, MDPI, vol. 16(2), pages 1-41, January.
    2. Ivan Ferretti & Matteo Camparada & Lucio Enrico Zavanella, 2022. "Queuing Theory-Based Design Methods for the Definition of Power Requirements in Manufacturing Systems," Energies, MDPI, vol. 15(20), pages 1-14, October.
    3. José Luis Ruiz Duarte & Neng Fan, 2022. "Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies," Energies, MDPI, vol. 15(7), pages 1-24, March.
    4. Bjørndal, Endre & Bjørndal, Mette Helene & Coniglio, Stefano & Körner, Marc-Fabian & Leinauer, Christina & Weibelzahl, Martin, 2023. "Energy storage operation and electricity market design: On the market power of monopolistic storage operators," European Journal of Operational Research, Elsevier, vol. 307(2), pages 887-909.
    5. Anjos, Miguel F. & Brotcorne, Luce & Gomez-Herrera, Juan A., 2021. "Optimal setting of time-and-level-of-use prices for an electricity supplier," Energy, Elsevier, vol. 225(C).
    6. Bruno Mota & Luis Gomes & Pedro Faria & Carlos Ramos & Zita Vale & Regina Correia, 2021. "Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events," Energies, MDPI, vol. 14(2), pages 1-14, January.
    7. Li, Yuxin & Wang, Jiangjiang & Zhou, Yuan & Wei, Changqi & Guan, Zhimin & Chen, Haiyue, 2023. "Multi-dimension day-ahead scheduling optimization of a community-scale solar-driven CCHP system with demand-side management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    8. Gómez, Javier & Chicaiza, William D. & Escaño, Juan M. & Bordons, Carlos, 2023. "A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms," Renewable Energy, Elsevier, vol. 215(C).
    9. Levorato, Mario & Figueiredo, Rosa & Frota, Yuri, 2022. "Exact solutions for the two-machine robust flow shop with budgeted uncertainty," European Journal of Operational Research, Elsevier, vol. 300(1), pages 46-57.
    10. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    11. Zhang, Mengling & Jiao, Zihao & Ran, Lun & Zhang, Yuli, 2023. "Optimal energy and reserve scheduling in a renewable-dominant power system," Omega, Elsevier, vol. 118(C).
    12. Markus Hilbert & Andreas Dellnitz & Andreas Kleine, 2023. "Production planning under RTP, TOU and PPA considering a redox flow battery storage system," Annals of Operations Research, Springer, vol. 328(2), pages 1409-1436, September.
    13. Mohamed Habib Jabeur & Sonia Mahjoub & Cyril Toublanc, 2023. "Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study," Energies, MDPI, vol. 16(14), pages 1-24, July.

    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:ejores:v:281:y:2020:i:1:p:186-200. 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.

    We have no bibliographic references for this item. You can help adding them by using 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/locate/eor .

    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.