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Time-of-use based electricity cost of manufacturing systems: Modeling and monotonicity analysis

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  • Wang, Yong
  • Li, Lin

Abstract

Traditionally, manufacturing enterprises pay flat rates for each kiloWatt-hour (kWh) of electricity consumed. Newly available electric tariffs that charge both energy consumption (in kWh) and peak demand (in kiloWatts, i.e., kW) with varying time-of-use (TOU) rates have started to gain popularity. In this paper, the per-product electricity cost as a function of manufacturing system parameters and the TOU rates is modeled. The contributions of both electricity energy consumption and peak demand are combined to formulate the electricity cost of manufacturing systems with multiple machines and buffers. New knowledge of the effects of various modeling parameters on the electricity cost is acquired through monotonicity analysis. The formulated model is utilized to answer the following two questions facing manufacturers: With the availability of TOU rates in mind, is switching from the flat rates to the TOU rates economically sound? What changes can be made on electric use routines to take advantage of the TOU rates? The findings based on case studies show that with appropriate adjustment of production routines, a significant saving of up to 24.8% of the per-product electricity cost can be achieved by adopting the TOU rates.

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  • 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.
    • Handle: RePEc:eee:proeco:v:156:y:2014:i:c:p:246-259
    DOI: 10.1016/j.ijpe.2014.06.015
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    Cited by:

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    2. Sun, Zeyi & Li, Lin & Bego, Andres & Dababneh, Fadwa, 2015. "Customer-side electricity load management for sustainable manufacturing systems utilizing combined heat and power generation system," International Journal of Production Economics, Elsevier, vol. 165(C), pages 112-119.
    3. Cui, Weiwei & Li, Lin, 2018. "A game-theoretic approach to optimize the Time-of-Use pricing considering customer behaviors," International Journal of Production Economics, Elsevier, vol. 201(C), pages 75-88.
    4. Park, Myoung-Ju & Ham, Andy, 2022. "Energy-aware flexible job shop scheduling under time-of-use pricing," International Journal of Production Economics, Elsevier, vol. 248(C).
    5. 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).
    6. Salahi, Niloofar & Jafari, Mohsen A., 2016. "Energy-Performance as a driver for optimal production planning," Applied Energy, Elsevier, vol. 174(C), pages 88-100.
    7. Yang, Changhui & Meng, Chen & Zhou, Kaile, 2018. "Residential electricity pricing in China: The context of price-based demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2870-2878.
    8. Dababneh, Fadwa & Li, Lin & Sun, Zeyi, 2016. "Peak power demand reduction for combined manufacturing and HVAC system considering heat transfer characteristics," International Journal of Production Economics, Elsevier, vol. 177(C), pages 44-52.
    9. 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.
    10. 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.

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