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An MILP framework for optimizing demand response operation of air separation units

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  • Kelley, Morgan T.
  • Pattison, Richard C.
  • Baldick, Ross
  • Baldea, Michael

Abstract

Peaks in renewable electricity generation and consumer demand are desynchronized in time, posing a challenge for grid operators. Industrial demand response (DR) has emerged as a candidate for mitigating this variability. In this paper, we demonstrate the application of DR to an air separation unit (ASU). We develop a novel optimal production scheduling framework that accounts for day-ahead electricity prices to modulate the grid load presented by the plant. We account for the dynamics of the plant using a novel dynamic modeling strategy, which allows us to formulate the corresponding optimization problem as a mixed integer linear program (MILP). Further, we present a new relaxation scheme that affords fast solutions of this MILP. Extensive simulation results show significant reductions in operating costs (that benefit the plant) and reductions in peak power demand (that benefit the grid).

Suggested Citation

  • Kelley, Morgan T. & Pattison, Richard C. & Baldick, Ross & Baldea, Michael, 2018. "An MILP framework for optimizing demand response operation of air separation units," Applied Energy, Elsevier, vol. 222(C), pages 951-966.
    • Handle: RePEc:eee:appene:v:222:y:2018:i:c:p:951-966
    DOI: 10.1016/j.apenergy.2017.12.127
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    References listed on IDEAS

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    4. Kong, Fulin & Liu, Yuxin & Tong, Lige & Guo, Wei & Qiu, Yinan & Wang, Li, 2022. "Optimization of co-production air separation unit based on MILP under multi-product deterministic demand," Applied Energy, Elsevier, vol. 325(C).
    5. Kong, Fulin & Liu, Yuxin & Shen, Minghai & Tong, Lige & Yin, Shaowu & Wang, Li & Ding, Yulong, 2023. "A novel economic scheduling of multi-product deterministic demand for co-production air separation system with liquid air energy storage," Renewable Energy, Elsevier, vol. 209(C), pages 533-545.
    6. Che, Gelegen & Zhang, Yanyan & Tang, Lixin & Zhao, Shengnan, 2023. "A deep reinforcement learning based multi-objective optimization for the scheduling of oxygen production system in integrated iron and steel plants," Applied Energy, Elsevier, vol. 345(C).
    7. Laing, Harry & O'Malley, Chris & Browne, Anthony & Rutherford, Tony & Baines, Tony & Moore, Andrew & Black, Ken & Willis, Mark J., 2022. "Optimisation of energy usage and carbon emissions monitoring using MILP for an advanced anaerobic digester plant," Energy, Elsevier, vol. 256(C).
    8. Kasper, Lukas & Schwarzmayr, Paul & Birkelbach, Felix & Javernik, Florian & Schwaiger, Michael & Hofmann, René, 2024. "A digital twin-based adaptive optimization approach applied to waste heat recovery in green steel production: Development and experimental investigation," Applied Energy, Elsevier, vol. 353(PB).
    9. Otashu, Joannah I. & Baldea, Michael, 2020. "Scheduling chemical processes for frequency regulation," Applied Energy, Elsevier, vol. 260(C).
    10. Markus Fleschutz & Markus Bohlayer & Marco Braun & Michael D. Murphy, 2023. "From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company," Papers 2301.07997, arXiv.org.
    11. Zeng, Lanting & Qiu, Dawei & Sun, Mingyang, 2022. "Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks," Applied Energy, Elsevier, vol. 324(C).
    12. Xiao, Dongliang & Lin, Zhenjia & Chen, Haoyong & Hua, Weiqi & Yan, Jinyue, 2024. "Windfall profit-aware stochastic scheduling strategy for industrial virtual power plant with integrated risk-seeking/averse preferences," Applied Energy, Elsevier, vol. 357(C).
    13. Abdollahi, Elnaz & Wang, Haichao & Lahdelma, Risto, 2019. "Parametric optimization of long-term multi-area heat and power production with power storage," Applied Energy, Elsevier, vol. 235(C), pages 802-812.
    14. Cao, Qiang & Sun, Zheng & Li, Zimu & Luan, Mingkai & Tang, Xiao & Li, Peng & Jiang, Zhenhua & Wei, Li, 2019. "Reduction of real gas losses with a DC flow in the regenerator of the refrigeration cycle," Applied Energy, Elsevier, vol. 235(C), pages 139-146.

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