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Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources

Author

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  • Weiwei Cui
  • Lin Li
  • Zhiqiang Lu

Abstract

Environmentally friendly energy resources open a new opportunity to tackle the problem of energy security and climate change arising from wide use of fossil fuels. This paper focuses on optimizing the allocation of the energy generated by the renewable energy system to minimize the total electricity cost for sustainable manufacturing systems under time‐of‐use tariff by clipping the peak demand. A rolling horizon approach is adopted to handle the uncertainty caused by the weather change. A nonlinear mathematical programming model is established for each decision epoch based on the predicted energy generation and the probability distribution of power demand in the manufacturing plant. The objective function of the model is shown to be convex, Lipchitz‐continuous, and subdifferentiable. A generalized benders decomposition method based on the primal‐dual subgradient descent algorithm is proposed to solve the model. A series of numerical experiments is conducted to show the effectiveness of the solution approach and the significant benefits of using the renewable energy resources.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:navres:v:66:y:2019:i:2:p:154-173
    DOI: 10.1002/nav.21830
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    as
    1. Liu, Kun & Guan, Xiaohong & Gao, Feng & Zhai, Qiaozhu & Wu, Jiang, 2015. "Self-balancing robust scheduling with flexible batch loads for energy intensive corporate microgrid," Applied Energy, Elsevier, vol. 159(C), pages 391-400.
    2. Lixin Tang & Feng Li & Jiyin Liu, 2015. "Integrated scheduling of loading and transportation with tractors and semitrailers separated," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(5), pages 416-433, August.
    3. Zidan, Aboelsood & Gabbar, Hossam A. & Eldessouky, Ahmed, 2015. "Optimal planning of combined heat and power systems within microgrids," Energy, Elsevier, vol. 93(P1), pages 235-244.
    4. Abedini, Mohammad & Moradi, Mohammad H. & Hosseinian, S. Mahdi, 2016. "Optimal management of microgrids including renewable energy scources using GPSO-GM algorithm," Renewable Energy, Elsevier, vol. 90(C), pages 430-439.
    5. Torriti, Jacopo, 2012. "Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy," Energy, Elsevier, vol. 44(1), pages 576-583.
    6. Cheng-Hsiang Liu, 2016. "Mathematical programming formulations for single-machine scheduling problems while considering renewable energy uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1122-1133, February.
    7. Mansouri, S. Afshin & Aktas, Emel & Besikci, Umut, 2016. "Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption," European Journal of Operational Research, Elsevier, vol. 248(3), pages 772-788.
    8. Tongdan Jin & Ying Yu & Elsayed Elsayed, 2015. "Reliability and quality control for distributed wind/solar energy integration: a multi-criteria approach," IISE Transactions, Taylor & Francis Journals, vol. 47(10), pages 1122-1138, October.
    9. Mitra, Sumit & Sun, Lige & Grossmann, Ignacio E., 2013. "Optimal scheduling of industrial combined heat and power plants under time-sensitive electricity prices," Energy, Elsevier, vol. 54(C), pages 194-211.
    10. Cheng, William Y.Y. & Liu, Yubao & Liu, Yuewei & Zhang, Yongxin & Mahoney, William P. & Warner, Thomas T., 2013. "The impact of model physics on numerical wind forecasts," Renewable Energy, Elsevier, vol. 55(C), pages 347-356.
    11. 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.
    12. Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
    13. Ma, Xiandong & Wang, Yifei & Qin, Jianrong, 2013. "Generic model of a community-based microgrid integrating wind turbines, photovoltaics and CHP generations," Applied Energy, Elsevier, vol. 112(C), pages 1475-1482.
    14. Çağrı Latifoğlu & Pietro Belotti & Lawrence V. Snyder, 2013. "Models for production planning under power interruptions," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(5), pages 413-431, August.
    15. 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.
    16. Lu Zhen & Shuaian Wang & Kai Wang, 2016. "Terminal allocation problem in a transshipment hub considering bunker consumption," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(7), pages 529-548, October.
    17. Mohan, Vivek & Singh, Jai Govind & Ongsakul, Weerakorn, 2015. "An efficient two stage stochastic optimal energy and reserve management in a microgrid," Applied Energy, Elsevier, vol. 160(C), pages 28-38.
    18. M. Mohsen Moarefdoost & Lawrence V. Snyder, 2015. "Generation and storage dispatch in electricity networks with generator disruptions," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(6), pages 493-511, September.
    19. Kim, Jong Suk & Edgar, Thomas F., 2014. "Optimal scheduling of combined heat and power plants using mixed-integer nonlinear programming," Energy, Elsevier, vol. 77(C), pages 675-690.
    20. Ding, Jian-Ya & Song, Shiji & Wu, Cheng, 2016. "Carbon-efficient scheduling of flow shops by multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 758-771.
    21. Faruqui, Ahmad & Malko, J.Robert, 1983. "The residential demand for electricity by time-of-use: A survey of twelve experiments with peak load pricing," Energy, Elsevier, vol. 8(10), pages 781-795.
    22. Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
    23. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    24. 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.
    25. Zheng, Huan-yu & Wang, Ling, 2015. "Reduction of carbon emissions and project makespan by a Pareto-based estimation of distribution algorithm," International Journal of Production Economics, Elsevier, vol. 164(C), pages 421-432.
    26. Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
    27. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    28. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
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    Cited by:

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    3. Govindan, Kannan & Rajeev, A. & Padhi, Sidhartha S. & Pati, Rupesh K., 2020. "Supply chain sustainability and performance of firms: A meta-analysis of the literature," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    4. Norouzi, Farshid & Hoppe, Thomas & Elizondo, Laura Ramirez & Bauer, Pavol, 2022. "A review of socio-technical barriers to Smart Microgrid development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    5. Quetzalcoatl Hernandez-Escobedo & Javier Garrido & Fernando Rueda-Martinez & Gerardo Alcalá & Alberto-Jesus Perea-Moreno, 2019. "Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico," Energies, MDPI, vol. 12(12), pages 1-22, June.

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