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A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty


  • Charitopoulos, Vassilis M.
  • Dua, Vivek


Process and energy models provide an invaluable tool for design, analysis and optimisation. These models are usually based upon a number of assumptions, simplifications and approximations, thereby introducing uncertainty in the model predictions. Making model based optimal decisions under uncertainty is therefore a challenging task. This issue is further exacerbated when more than one objective is to be optimised simultaneously, resulting in a Multi-Objective Optimisation (MO2) problem. Even though, some methods have been proposed for MO2 problems under uncertainty, two separate optimisation techniques are employed; one to address the multi-objective aspect and another to take into account uncertainty. In the present work, we propose a unified optimisation framework for linear MO2 problems, in which the uncertainty and the multiple objectives are modelled as varying parameters. The MO2 under uncertainty problem (MO2U2) is thus reformulated and solved as a multi-parametric programming problem. The solution of the multi-parametric programming problem provides the optimal solution as a set of parametric profiles.

Suggested Citation

  • Charitopoulos, Vassilis M. & Dua, Vivek, 2017. "A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty," Applied Energy, Elsevier, vol. 186(P3), pages 539-548.
  • Handle: RePEc:eee:appene:v:186:y:2017:i:p3:p:539-548
    DOI: 10.1016/j.apenergy.2016.05.082

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    References listed on IDEAS

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    Cited by:

    1. Bistline, John E. & Comello, Stephen D. & Sahoo, Anshuman, 2018. "Managerial flexibility in levelized cost measures: A framework for incorporating uncertainty in energy investment decisions," Energy, Elsevier, vol. 151(C), pages 211-225.
    2. Geng, Zhaowei & Conejo, Antonio J. & Chen, Qixin & Xia, Qing & Kang, Chongqing, 2017. "Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production," Applied Energy, Elsevier, vol. 193(C), pages 540-549.
    3. repec:eee:appene:v:235:y:2019:i:c:p:1427-1446 is not listed on IDEAS
    4. repec:eee:energy:v:164:y:2018:i:c:p:1011-1020 is not listed on IDEAS
    5. repec:eee:appene:v:253:y:2019:i:c:15 is not listed on IDEAS
    6. Nikula, Riku-Pekka & Ruusunen, Mika & Leiviskä, Kauko, 2016. "Data-driven framework for boiler performance monitoring," Applied Energy, Elsevier, vol. 183(C), pages 1374-1388.


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