IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v186y2017ip3p539-548.html
   My bibliography  Save this article

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

Author

Listed:
  • Charitopoulos, Vassilis M.
  • Dua, Vivek

Abstract

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916306791
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. G. Klein & H. Moskowitz & A. Ravindran, 1990. "Interactive Multiobjective Optimization Under Uncertainty," Management Science, INFORMS, vol. 36(1), pages 58-75, January.
    3. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    4. Luo, Xianglong & Hu, Jiahao & Zhao, Jun & Zhang, Bingjian & Chen, Ying & Mo, Songping, 2014. "Multi-objective optimization for the design and synthesis of utility systems with emission abatement technology concerns," Applied Energy, Elsevier, vol. 136(C), pages 1110-1131.
    5. Tomas Gal & Josef Nedoma, 1972. "Multiparametric Linear Programming," Management Science, INFORMS, vol. 18(7), pages 406-422, March.
    6. Barteczko-Hibbert, Christian & Bonis, Ioannis & Binns, Michael & Theodoropoulos, Constantinos & Azapagic, Adisa, 2014. "A multi-period mixed-integer linear optimisation of future electricity supply considering life cycle costs and environmental impacts," Applied Energy, Elsevier, vol. 133(C), pages 317-334.
    7. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
    8. Hu, Mengqi & Cho, Heejin, 2014. "A probability constrained multi-objective optimization model for CCHP system operation decision support," Applied Energy, Elsevier, vol. 116(C), pages 230-242.
    9. Li, Fang-Fang & Qiu, Jun, 2016. "Multi-objective optimization for integrated hydro–photovoltaic power system," Applied Energy, Elsevier, vol. 167(C), pages 377-384.
    10. P. L. Yuf & M. Zeleny, 1976. "Linear Multiparametric Programming by Multicriteria Simplex Method," Management Science, INFORMS, vol. 23(2), pages 159-170, October.
    11. Zhang, Di & Evangelisti, Sara & Lettieri, Paola & Papageorgiou, Lazaros G., 2015. "Optimal design of CHP-based microgrids: Multiobjective optimisation and life cycle assessment," Energy, Elsevier, vol. 85(C), pages 181-193.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:186:y:2017:i:p3:p:539-548. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.