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Operational optimization of a grid-connected factory with onsite photovoltaic and battery storage systems

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  • Zhang, Hao
  • Cai, Jie
  • Fang, Kan
  • Zhao, Fu
  • Sutherland, John W.

Abstract

Driven by fast advancements in wind and photovoltaic (PV) technologies, onsite renewable electricity generation is becoming attractive to manufacturers since they are able to reduce electricity purchases from the grid and may lower their electricity costs. This paper proposes a methodology to minimize the electricity cost of a grid-connected factory that also has onsite solar power generation and battery storage. Purchases from the grid are subject to time-of-use electricity rate schedules. The problem is formulated asa mixed-integer programming problem and GAMS is used to find the optimal manufacturing and onsite energy flow schedules that have the minimal electricity cost. A case study with one hybrid flow shop, onsite PV power generation, and a battery was used to test the proposed method. Testing results showed that the factory’s electricity cost can be reduced by 54.0% under summer TOU rate on a typical day while a 0.7% electricity cost reduction can be achieved for a representative day under a winter TOU rate. An annual electricity cost savings of 28.1% can be obtained with the optimal schedules. In addition, a parametric study incorporating the optimal schedules was performed to understand the economic performances associated with different PV capacity and battery bank size for the factory.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:1538-1547
    DOI: 10.1016/j.apenergy.2017.08.140
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    5. Lu-Miao Li, Peng Zhou, and Wen Wen, 2023. "Distributed Renewable Energy Investment: The Effect of Time-of-Use Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    6. Ma, Tao & Zhang, Yijie & Gu, Wenbo & Xiao, Gang & Yang, Hongxing & Wang, Shuxiao, 2022. "Strategy comparison and techno-economic evaluation of a grid-connected photovoltaic-battery system," Renewable Energy, Elsevier, vol. 197(C), pages 1049-1060.
    7. Ghorbanzadeh, Masoumeh & Ranjbar, Mohammad, 2023. "Energy-aware production scheduling in the flow shop environment under sequence-dependent setup times, group scheduling and renewable energy constraints," European Journal of Operational Research, Elsevier, vol. 307(2), pages 519-537.
    8. Cai, Jie & Zhang, Hao & Jin, Xing, 2019. "Aging-aware predictive control of PV-battery assets in buildings," Applied Energy, Elsevier, vol. 236(C), pages 478-488.
    9. 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.
    10. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    11. Walden, Jasper V.M. & Bähr, Martin & Glade, Anselm & Gollasch, Jens & Tran, A. Phong & Lorenz, Tom, 2023. "Nonlinear operational optimization of an industrial power-to-heat system with a high temperature heat pump, a thermal energy storage and wind energy," Applied Energy, Elsevier, vol. 344(C).
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    13. Belsky, A.A. & Glukhanich, D.Y. & Carrizosa, M.J. & Starshaia, V.V., 2022. "Analysis of specifications of solar photovoltaic panels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).

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