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Load profile analysis for reducing energy demands of production systems in non-production times

Author

Listed:
  • Dehning, Patrick
  • Blume, Stefan
  • Dér, Antal
  • Flick, Dominik
  • Herrmann, Christoph
  • Thiede, Sebastian

Abstract

Intensifying market pressure, extended environmental legislations, increasing environmental consciousness and rising energy prices are a major concern for production companies worldwide. Production of goods is responsible for about one-third of the global greenhouse gas emissions. As a consequence the energy demand in production and with this the energy costs for the production of a product are moving more into focus of decision makers. Depending on the shift and working system of a company, the energy demand during planned non-production times like free shifts, weekends or holidays can be significant. However, a lack of knowledge about realistically achievable electrical load levels in non-production times due to missing benchmarks can be observed in practice. As a consequence, related energy saving potentials remain undetected. Against this background, this paper presents a methodology to analyze the electrical load during non-production times using load duration curves. Performance indicators are developed allowing for a comparison between factories in order to identify energy saving potentials. Within this paper a tool is developed to easily compare different automotive factories and tested using real data of two large car manufacturers.

Suggested Citation

  • Dehning, Patrick & Blume, Stefan & Dér, Antal & Flick, Dominik & Herrmann, Christoph & Thiede, Sebastian, 2019. "Load profile analysis for reducing energy demands of production systems in non-production times," Applied Energy, Elsevier, vol. 237(C), pages 117-130.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:117-130
    DOI: 10.1016/j.apenergy.2019.01.047
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    Cited by:

    1. Mitali Sarkar & Sungjun Kim & Jihed Jemai & Baishakhi Ganguly & Biswajit Sarkar, 2019. "An Application of Time-Dependent Holding Costs and System Reliability in a Multi-Item Sustainable Economic Energy Efficient Reliable Manufacturing System," Energies, MDPI, vol. 12(15), pages 1-19, July.
    2. Wen, Xuanhao & Cao, Huajun & Li, Hongcheng & Zheng, Jie & Ge, Weiwei & Chen, Erheng & Gao, Xi & Hon, Bernard, 2022. "A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques," Energy, Elsevier, vol. 255(C).
    3. Lange, Christopher & Rueß, Alexandra & Nuß, Andreas & Öchsner, Richard & März, Martin, 2020. "Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm," Applied Energy, Elsevier, vol. 280(C).
    4. Wen, Xuanhao & Cao, Huajun & Hon, Bernard & Chen, Erheng & Li, Hongcheng, 2021. "Energy value mapping: A novel lean method to integrate energy efficiency into production management," Energy, Elsevier, vol. 217(C).
    5. Mitali Sarkar & Biswajit Sarkar, 2019. "Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management," Energies, MDPI, vol. 12(11), pages 1-16, May.
    6. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    7. Asif Iqbal Malik & Byung Soo Kim, 2020. "A Constrained Production System Involving Production Flexibility and Carbon Emissions," Mathematics, MDPI, vol. 8(2), pages 1-21, February.

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