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• W3210127575 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." @default.
• W3210127575 created "2021-11-08" @default.
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• W3210127575 date "2021-11-02" @default.
• W3210127575 modified "2023-09-24" @default.
• W3210127575 title "Optimisation of the Operation of an Industrial Power Plant under Steam Demand Uncertainty" @default.
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• W3210127575 doi "https://doi.org/10.3390/en14217213" @default.
• W3210127575 hasPublicationYear "2021" @default.
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