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Data-driven real-time price-based demand response for industrial facilities energy management

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  • Lu, Renzhi
  • Bai, Ruichang
  • Huang, Yuan
  • Li, Yuting
  • Jiang, Junhui
  • Ding, Yuemin

Abstract

Recent advances in smart grid technologies have highlighted demand response (DR) as an important tool to alleviate electricity demand–supply mismatches. In this paper, a real-time price (RTP)-based DR algorithm is proposed for industrial facilities, aiming to minimize the electricity cost while satisfying production requirements. In particular, due to future price uncertainties, a data-driven approach is adopted to forecast the future unknown prices for supporting global time horizon optimization, which is realized by long short-term memory recurrent neural network (LSTM RNN). With the aid of predicted prices, the industrial facility energy management is formulated as a mixed integer linear programming (MILP) problem, which is then solved by Gurobi over a rolling horizon basis. Finally, an entire practical steel powder manufacturing process is selected as a case study to verify the RTP-based DR scheme. Numerical simulation results show that the proposed scheme is able to effectively shift energy consumption from peak to off-peak periods and reduce the electricity cost of the facility, while satisfying all of the operating constraints. The performance of the presented data-driven RTP forecasting approach is compared to different prediction methods, and error sensitivity analyses are also conducted to evaluate the impact of the RTP uncertainties and the robustness of the proposed RTP-based DR algorithm. Moreover, the DR capability to RTPs is investigated.

Suggested Citation

  • Lu, Renzhi & Bai, Ruichang & Huang, Yuan & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2021. "Data-driven real-time price-based demand response for industrial facilities energy management," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316779
    DOI: 10.1016/j.apenergy.2020.116291
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    6. Norouzi, Mohammadali & Aghaei, Jamshid & Niknam, Taher & Alipour, Mohammadali & Pirouzi, Sasan & Lehtonen, Matti, 2023. "Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting," Applied Energy, Elsevier, vol. 348(C).
    7. Yun, Lingxiang & Xiao, Minkun & Li, Lin, 2022. "Vehicle-to-manufacturing (V2M) system: A novel approach to improve energy demand flexibility for demand response towards sustainable manufacturing," Applied Energy, Elsevier, vol. 323(C).
    8. Yun, Lingxiang & Li, Lin & Ma, Shuaiyin, 2022. "Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment," Applied Energy, Elsevier, vol. 310(C).
    9. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    10. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
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    12. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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