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A Price-Based Demand Response Scheme for Discrete Manufacturing in Smart Grids

Author

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  • Zhe Luo

    (Department of Electronic Systems Engineering, Hanyang University, 1271 Sa 3-dong, Sangnok-gu, Ansan-Si, Gyeonggi-do 426-791, Korea)

  • Seung-Ho Hong

    (Department of Electronic Systems Engineering, Hanyang University, 1271 Sa 3-dong, Sangnok-gu, Ansan-Si, Gyeonggi-do 426-791, Korea)

  • Jong-Beom Kim

    (Department of Electronic Systems Engineering, Hanyang University, 1271 Sa 3-dong, Sangnok-gu, Ansan-Si, Gyeonggi-do 426-791, Korea)

Abstract

Demand response (DR) is a key technique in smart grid (SG) technologies for reducing energy costs and maintaining the stability of electrical grids. Since manufacturing is one of the major consumers of electrical energy, implementing DR in factory energy management systems (FEMSs) provides an effective way to manage energy in manufacturing processes. Although previous studies have investigated DR applications in process manufacturing, they were not conducted for discrete manufacturing. In this study, the state-task network (STN) model is implemented to represent a discrete manufacturing system. On this basis, a DR scheme with a specific DR algorithm is applied to a typical discrete manufacturing—automobile manufacturing—and operational scenarios are established for the stamping process of the automobile production line. The DR scheme determines the optimal operating points for the stamping process using mixed integer linear programming (MILP). The results show that parts of the electricity demand can be shifted from peak to off-peak periods, reducing a significant overall energy costs without degrading production processes.

Suggested Citation

  • Zhe Luo & Seung-Ho Hong & Jong-Beom Kim, 2016. "A Price-Based Demand Response Scheme for Discrete Manufacturing in Smart Grids," Energies, MDPI, vol. 9(8), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:650-:d:76140
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    References listed on IDEAS

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    Cited by:

    1. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    2. 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.
    3. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    4. Yu, Mengmeng & Lu, Renzhi & Hong, Seung Ho, 2016. "A real-time decision model for industrial load management in a smart grid," Applied Energy, Elsevier, vol. 183(C), pages 1488-1497.
    5. Walmsley, Timothy Gordon & Philipp, Matthias & Picón-Núñez, Martín & Meschede, Henning & Taylor, Matthew Thomas & Schlosser, Florian & Atkins, Martin John, 2023. "Hybrid renewable energy utility systems for industrial sites: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    6. Allegra De Filippo & Michele Lombardi & Michela Milano, 2017. "User-Aware Electricity Price Optimization for the Competitive Market," Energies, MDPI, vol. 10(9), pages 1-23, September.
    7. Shin-Yeu Lin & Ai-Chih Lin, 2016. "Risk-Limiting Scheduling of Optimal Non-Renewable Power Generation for Systems with Uncertain Power Generation and Load Demand," Energies, MDPI, vol. 9(11), pages 1-16, October.

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