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Implementing factory demand response via onsite renewable energy: a design-of-experiment approach

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  • Victor Santana-Viera
  • Jesus Jimenez
  • Tongdan Jin
  • Jose Espiritu

Abstract

This paper proposes the implementation of demand response (DR) programmes in large manufacturing facilities featuring distributed wind and solar energy. Manufacturing facilities are high consumers of electric power. For this reason, these facilities usually pay exorbitant utility bills, which could be as much as $10--20 million per year. A high consumption of electricity also means that upstream fossil-fuelled power plants must release thousands of metric tonnes of carbon annually during the generation of electricity. DR contracts offer a lower utility rate in return for a load reduction during contingent events (i.e. peak hours). This paper covers the modelling and implementation of an interruptible/curtailable DR programme participated by a manufacturer that possesses onsite renewable generation units. These complementary energy resources allow the manufacturer to meet the curtailment requirements without causing any major electricity shortage that adversely affects the normal production schedule. We developed a stochastic programming model to determine the capacity of the wind turbine and solar panels that maximise the DR programme savings. The optimal solutions are derived based on central composite design methodology.

Suggested Citation

  • Victor Santana-Viera & Jesus Jimenez & Tongdan Jin & Jose Espiritu, 2015. "Implementing factory demand response via onsite renewable energy: a design-of-experiment approach," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7034-7048, December.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:23:p:7034-7048
    DOI: 10.1080/00207543.2014.957877
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    Cited by:

    1. Binbin Li & Yu Tian & Fred Chen & Tongdan Jin, 2017. "Toward net-zero carbon manufacturing operations: an onsite renewables solution," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(3), pages 308-321, March.
    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. 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.
    4. Hiromasa Ijuin & Satoshi Yamada & Tetsuo Yamada & Masato Takanokura & Masayuki Matsui, 2022. "Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo," Energies, MDPI, vol. 15(13), pages 1-23, June.
    5. Zhang, Hao & Cai, Jie & Fang, Kan & Zhao, Fu & Sutherland, John W., 2017. "Operational optimization of a grid-connected factory with onsite photovoltaic and battery storage systems," Applied Energy, Elsevier, vol. 205(C), pages 1538-1547.
    6. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
    7. Ursavas, Evrim, 2017. "A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea," European Journal of Operational Research, Elsevier, vol. 258(2), pages 703-714.
    8. 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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