Optimisation of the Operation of an Industrial Power Plant under Steam Demand Uncertainty

Keivan Rahimi-Adli, Egidio Leo, Benedikt Beisheim and Sebastian Engell
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Keivan Rahimi-Adli: INEOS Manufacturing Deutschland GmbH, Alte Strasse 201, 50769 Köln, Germany
Egidio Leo: Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering, Technische Universität Dortmund, Emil-Figge-Str. 70, 44221 Dortmund, Germany
Benedikt Beisheim: Bayer AG, Engineering and Technology, 51368 Leverkusen, Germany
Sebastian Engell: Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering, Technische Universität Dortmund, Emil-Figge-Str. 70, 44221 Dortmund, Germany

Energies, 2021, vol. 14, issue 21, 1-28

Abstract: The operation of on-site power plants in the chemical industry is typically determined by the steam demand of the production plants. This demand is uncertain due to deviations from the production plan and fluctuations in the operation of the plants. The steam demand uncertainty can result in an inefficient operation of the power plant due to a surplus or deficiency of steam that is needed to balance the steam network. In this contribution, it is proposed to use two-stage stochastic programming on a moving horizon to cope with the uncertainty. In each iteration of the moving horizon scheme, the model parameters are updated according to the new information acquired from the plants and the optimisation is re-executed. Hedging against steam demand uncertainty results in a reduction of the fuel consumption and a more economic generation of electric power, which can result in significant savings in the operating cost of the power plant. Moreover, unplanned load reductions due to lack of steam can be avoided. The application of the new approach is demonstrated for the on-site power plant of INEOS in Köln, and significant savings are reported in exemplary simulations.

Keywords: combined heat and power plants; industrial power plant; steam demand uncertainty; scheduling; stochastic optimisation; optimisation on a moving horizon (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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