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Retrospective and predictive optimal scheduling of nitrogen liquefier units and the effect of renewable generation

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  • Cummings, Thomas
  • Adamson, Richard
  • Sugden, Andrew
  • Willis, Mark J.

Abstract

The construction and application of a multiple nitrogen liquefier unit (NLU) optimal scheduling tool is discussed. Constrained by customer demands and subject to electricity spot market prices over a week-ahead horizon, a retrospective optimiser (RO) determines the minimum scheduling costs. Plant start-up penalties and inter-site optimisation capabilities are incorporated into the optimisation model to emulate realistic operational flexibilities and costs. Using operational data, actual process schedules are compared to the RO results leading to improved process scheduling insights; such as increasing afternoon NLU operation during the spring to utilise lower power pricing caused by high solar generation. The RO is used to output a trackable load management key performance indicator to quantify potential and achieved scheduling improvements. Subsequently, correlations between renewable energy generation and spot market power prices are developed. Forecast pricing is used within a predictive optimiser (PO) to automatically generate an optimal schedule for the week ahead to meet projected customer demands. The RO provides potential hindsight savings of around 11%, and the PO up to 8% (representing significant cost savings for such energy intensive processes).

Suggested Citation

  • Cummings, Thomas & Adamson, Richard & Sugden, Andrew & Willis, Mark J., 2017. "Retrospective and predictive optimal scheduling of nitrogen liquefier units and the effect of renewable generation," Applied Energy, Elsevier, vol. 208(C), pages 158-170.
    • Handle: RePEc:eee:appene:v:208:y:2017:i:c:p:158-170
    DOI: 10.1016/j.apenergy.2017.10.055
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    References listed on IDEAS

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    1. Guthrie, Graeme & Videbeck, Steen, 2007. "Electricity spot price dynamics: Beyond financial models," Energy Policy, Elsevier, vol. 35(11), pages 5614-5621, November.
    2. Christodoulos Floudas & Xiaoxia Lin, 2005. "Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications," Annals of Operations Research, Springer, vol. 139(1), pages 131-162, October.
    3. Bentzen, Jan & Engsted, Tom, 2001. "A revival of the autoregressive distributed lag model in estimating energy demand relationships," Energy, Elsevier, vol. 26(1), pages 45-55.
    4. Adamson, Richard & Hobbs, Martin & Silcock, Andy & Willis, Mark J., 2017. "Steady-state optimisation of a multiple cryogenic air separation unit and compressor plant," Applied Energy, Elsevier, vol. 189(C), pages 221-232.
    5. Rafal Weron & Adam Misiorek, 2005. "Forecasting Spot Electricity Prices With Time Series Models," Econometrics 0504001, University Library of Munich, Germany.
    6. Lisa MH Hall & Alastair Buckley & Jose Mawyin, 2015. "Estimating the Impact of Wind Generation in the UK," Papers 1510.05854, arXiv.org, revised Nov 2015.
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