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Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment

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  • Yun, Lingxiang
  • Li, Lin
  • Ma, Shuaiyin

Abstract

Electricity demand response is proved to be a viable approach to address the peak demand, improve the power grid reliability, and facilitate the grid integration of renewable resources. The implementation of demand response management in the manufacturing sector, which is one of the main energy consumers, has received significant attention. The existing studies in this field have traditionally focused on maximizing the benefits of demand response through machine control and scheduling. However, due to the widespread adoption of fast-charging infrastructure for material handling equipment (MHE), the high-power demand of fast charging and the close coordination between the machines and MHE pose much-increased complexity in the energy management, which necessitates a more comprehensive demand response scheduling method for the integrated manufacturing and material handling system. In this study, an analytical manufacturing and material handling system model is proposed to obtain cost-effective production schedules under demand response. The interactions between machines and MHE, production throughput requirement, battery charging characteristic, and time-varying electricity pricing are jointly considered in the integrated model to help manufacturers reap substantial benefits of demand response. The effectiveness of the proposed method is validated through numerical case studies, where the results indicate a 15.1% energy cost reduction compared to the benchmark scheduling scheme.

Suggested Citation

  • Yun, Lingxiang & Li, Lin & Ma, Shuaiyin, 2022. "Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261922000368
    DOI: 10.1016/j.apenergy.2022.118550
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    References listed on IDEAS

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    Cited by:

    1. Valentina De Simone & Valentina Di Pasquale & Maria Elena Nenni & Salvatore Miranda, 2023. "Sustainable Production Planning and Control in Manufacturing Contexts: A Bibliometric Review," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    2. 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).
    3. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    4. Yun, Lingxiang & Xiao, Minkun & Li, Lin, 2022. "Vehicle-to-manufacturing (V2M) system: A novel approach to improve energy demand flexibility for demand response towards sustainable manufacturing," Applied Energy, Elsevier, vol. 323(C).
    5. Arabzadeh, Vahid & Miettinen, Panu & Kotilainen, Titta & Herranen, Pasi & Karakoc, Alp & Kummu, Matti & Rautkari, Lauri, 2023. "Urban vertical farming with a large wind power share and optimised electricity costs," Applied Energy, Elsevier, vol. 331(C).
    6. 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).

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