IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v230y2018icp1414-1424.html
   My bibliography  Save this article

Demand response scheduling in industrial asynchronous production lines constrained by available power and production rate

Author

Listed:
  • Desta, Alemayehu Addisu
  • Badis, Hakim
  • George, Laurent

Abstract

Energy efficiency in factories is a new paradigm arising with Demand Response (DR) programs that enables energy providers to ask their clients to reduce their power consumption for a given time. In this paper, we consider demand side management of an industrial customer who owns asynchronous production line systems. Our aim is to help the customer achieve a good trade-off between production rates and power consumption during DR events. To attain this objective, we develop a framework named DR-Mgmt to schedule activities of machines on production lines so that the number of simultaneously working machines is maximized while satisfying DR constraints. Our framework first models activities of a production line using a new approach based on temporal deterministic finite station machine concept, where each state represents machine status (working/idle) and transitions capture temporal changes. Then, the problem of finding an optimal schedule from all feasible schedules is handled by selecting the optimal set of state transitions. We adapt the well-known local search heuristic to find near-optimal transitions. Numerical results show a significant benefit of our approach on production rates during DR intervals. Compared to other approaches, the proposed framework performs best and improves production rates up to 70% in some cases with higher total power consumption. We also validate our work by a real case study.

Suggested Citation

  • Desta, Alemayehu Addisu & Badis, Hakim & George, Laurent, 2018. "Demand response scheduling in industrial asynchronous production lines constrained by available power and production rate," Applied Energy, Elsevier, vol. 230(C), pages 1414-1424.
    • Handle: RePEc:eee:appene:v:230:y:2018:i:c:p:1414-1424
    DOI: 10.1016/j.apenergy.2018.08.066
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918312248
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.08.066?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. 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.
    2. Savelsbergh, M. W. P., 1990. "An efficient implementation of local search algorithms for constrained routing problems," European Journal of Operational Research, Elsevier, vol. 47(1), pages 75-85, July.
    3. Zavanella, Lucio & Zanoni, Simone & Ferretti, Ivan & Mazzoldi, Laura, 2015. "Energy demand in production systems: A Queuing Theory perspective," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 393-400.
    4. Erez, Noam & Wool, Avishai, 2015. "Control variable classification, modeling and anomaly detection in Modbus/TCP SCADA systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 10(C), pages 59-70.
    5. Mansouri, S. Afshin & Aktas, Emel & Besikci, Umut, 2016. "Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption," European Journal of Operational Research, Elsevier, vol. 248(3), pages 772-788.
    6. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    7. Wang, Xiaonan & El-Farra, Nael H. & Palazoglu, Ahmet, 2017. "Optimal scheduling of demand responsive industrial production with hybrid renewable energy systems," Renewable Energy, Elsevier, vol. 100(C), pages 53-64.
    8. Zanoni, S. & Bettoni, L. & Glock, C. H., 2014. "Energy Implications in a Two-Stage Production System with Controllable Production Rates," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 62117, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Zanoni, Simone & Bettoni, Laura & Glock, Christoph H., 2014. "Energy implications in a two-stage production system with controllable production rates," International Journal of Production Economics, Elsevier, vol. 149(C), pages 164-171.
    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. 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).
    2. 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.
    3. 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.
    4. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    5. Lu, Renzhi & Li, Yi-Chang & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2020. "Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management," Applied Energy, Elsevier, vol. 276(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. Abbas Hamze & Yassine Ouazene & Nazir Chebbo & Imane Maatouk, 2019. "Multisources of Energy Contracting Strategy with an Ecofriendly Factor and Demand Uncertainties," Energies, MDPI, vol. 12(20), pages 1-24, October.
    2. Sven Schulz & Udo Buscher & Liji Shen, 2020. "Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices," Journal of Business Economics, Springer, vol. 90(9), pages 1315-1343, November.
    3. Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
    4. Kim, Hyunjung & Kim, Eungab, 2022. "A hybrid manufacturing system with demand for intermediate goods and controllable make-to-stock production rate," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1244-1257.
    5. Matthias Gerhard Wichmann & Christoph Johannes & Thomas Stefan Spengler, 2019. "An extension of the general lot-sizing and scheduling problem (GLSP) with time-dependent energy prices," Journal of Business Economics, Springer, vol. 89(5), pages 481-514, July.
    6. Beck, Fabian G. & Biel, Konstantin & Glock, Christoph H., 2019. "Integration of energy aspects into the economic lot scheduling problem," International Journal of Production Economics, Elsevier, vol. 209(C), pages 399-410.
    7. Wichmann, Matthias Gerhard & Johannes, Christoph & Spengler, Thomas Stefan, 2019. "Energy-oriented Lot-Sizing and Scheduling considering energy storages," International Journal of Production Economics, Elsevier, vol. 216(C), pages 204-214.
    8. Konstantin Biel & Christoph H. Glock, 2017. "Prerequisites of efficient decentralized waste heat recovery and energy storage in production planning," Journal of Business Economics, Springer, vol. 87(1), pages 41-72, January.
    9. Dhahri, Akrem & Gharbi, Ali & Ouhimmou, Mustapha, 2022. "Integrated production-delivery control policy for an unreliable manufacturing system and multiple retailers," International Journal of Production Economics, Elsevier, vol. 245(C).
    10. Bazan, Ehab & Jaber, Mohamad Y. & Zanoni, Simone, 2017. "Carbon emissions and energy effects on a two-level manufacturer-retailer closed-loop supply chain model with remanufacturing subject to different coordination mechanisms," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 394-408.
    11. Gila E. Fruchter & Hussein Naseraldin, 2021. "Coordinating Carbon Emissions via Production Quantities: A Differential Game Approach," Games, MDPI, vol. 12(1), pages 1-16, February.
    12. 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).
    13. Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).
    14. Lucio Enrico Zavanella & Beatrice Marchi & Simone Zanoni & Ivan Ferretti, 2019. "Energy considerations for the economic production quantity and the joint economic lot sizing," Journal of Business Economics, Springer, vol. 89(7), pages 845-865, September.
    15. Kim, Taebok & Glock, Christoph H., 2018. "Production planning for a two-stage production system with multiple parallel machines and variable production rates," International Journal of Production Economics, Elsevier, vol. 196(C), pages 284-292.
    16. Kotur, Dimitrije & Đurišić, Željko, 2017. "Optimal spatial and temporal demand side management in a power system comprising renewable energy sources," Renewable Energy, Elsevier, vol. 108(C), pages 533-547.
    17. Zavanella, Lucio & Zanoni, Simone & Ferretti, Ivan & Mazzoldi, Laura, 2015. "Energy demand in production systems: A Queuing Theory perspective," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 393-400.
    18. Glock, Christoph H. & Grosse, Eric H., 2021. "The impact of controllable production rates on the performance of inventory systems: A systematic review of the literature," European Journal of Operational Research, Elsevier, vol. 288(3), pages 703-720.
    19. Otashu, Joannah I. & Baldea, Michael, 2020. "Scheduling chemical processes for frequency regulation," Applied Energy, Elsevier, vol. 260(C).
    20. 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.

    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:230:y:2018:i:c:p:1414-1424. 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.