IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v146y2013i1p178-184.html
   My bibliography  Save this article

“Just-for-Peak” buffer inventory for peak electricity demand reduction of manufacturing systems

Author

Listed:
  • Fernandez, Mayela
  • Li, Lin
  • Sun, Zeyi

Abstract

The reduction of the electricity demand during peak periods is considered a main objective of electricity load management. It can relieve the financial pressure of the investment on the capacity expansion for the power grid in the United States. Compared to a great deal of research on commercial and residential building sectors, few studies on the electricity demand reduction during peak periods for industrial manufacturing systems have been conducted due to the concern of system throughput variation and the complexity of modern manufacturing systems. This paper presents a novel “Just-for-Peak” buffer inventory methodology to reduce the electricity consumption without compromising system throughput during peak periods for typical manufacturing systems with multiple machines and buffers. Nonlinear Integer Programming (NIP) formulation is used to establish the mathematical model. The optimal buffer inventory management policies and corresponding load management actions for the whole system are identified by minimizing the holding cost of the “Just-for-Peak” buffer inventory and energy consumption cost under the system throughput constraint throughout the production horizon. A numerical case study based on an automotive assembly line is used to illustrate the effectiveness of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:146:y:2013:i:1:p:178-184
    DOI: 10.1016/j.ijpe.2013.06.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527313002909
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2013.06.020?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. Salameh, M. K. & Ghattas, R. E., 2001. "Optimal just-in-time buffer inventory for regular preventive maintenance," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 157-161, December.
    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)

    Citations

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


    Cited by:

    1. 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.
    2. Wang, Yong & Li, Lin, 2016. "Critical peak electricity pricing for sustainable manufacturing: Modeling and case studies," Applied Energy, Elsevier, vol. 175(C), pages 40-53.
    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. 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.
    5. 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.
    6. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
    7. 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.
    8. Masmoudi, Oussama & Delorme, Xavier & Gianessi, Paolo, 2019. "Job-shop scheduling problem with energy consideration," International Journal of Production Economics, Elsevier, vol. 216(C), pages 12-22.
    9. Wang, Yong & Li, Lin, 2014. "Time-of-use based electricity cost of manufacturing systems: Modeling and monotonicity analysis," International Journal of Production Economics, Elsevier, vol. 156(C), pages 246-259.
    10. 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.
    11. Marcos Manoel Lopes Junior & Claudia Aparecida de Mattos & Fábio Lima, 2024. "Toward Cleaner Production by Evaluating Opportunities of Saving Energy in a Short-Cycle Time Flowshop," Sustainability, MDPI, vol. 16(6), pages 1-23, March.
    12. 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.

    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. 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.
    3. 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.
    4. Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
    5. Dimitrakos, T.D. & Kyriakidis, E.G., 2008. "A semi-Markov decision algorithm for the maintenance of a production system with buffer capacity and continuous repair times," International Journal of Production Economics, Elsevier, vol. 111(2), pages 752-762, February.
    6. A. Azadeh & M. Sheikhalishahi & S. Mortazavi & E. Ahmadi Jooghi, 2017. "Joint quality control and preventive maintenance strategy: a unique taguchi approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 123-134, March.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Zequeira, Romulo I. & Valdes, Jose E. & Berenguer, Christophe, 2008. "Optimal buffer inventory and opportunistic preventive maintenance under random production capacity availability," International Journal of Production Economics, Elsevier, vol. 111(2), pages 686-696, February.
    13. Kenne, J.-P. & Gharbi, A. & Beit, M., 2007. "Age-dependent production planning and maintenance strategies in unreliable manufacturing systems with lost sale," European Journal of Operational Research, Elsevier, vol. 178(2), pages 408-420, April.
    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. 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.
    16. 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.
    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. 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).
    19. 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.
    20. Berthaut, F. & Gharbi, A. & Kenné, J.-P. & Boulet, J.-F., 2010. "Improved joint preventive maintenance and hedging point policy," International Journal of Production Economics, Elsevier, vol. 127(1), pages 60-72, September.

    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:proeco:v:146:y:2013:i:1:p:178-184. 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/locate/ijpe .

    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.