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Combined cooling, heat and power planning under uncertainty

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  • Ersoz, Ibrahim
  • Colak, Uner

Abstract

The main purpose of this study is to derive a methodology for the analysis to determine the best CCHP (Combined Cooling Heat and Power) scheme in the presence of uncertainties differently from deterministic method. The proposed methodology helps decision makers see all the possible risks that impact the amortization of the system. Decisions for investments are generally taken by the conventional method, which relies on the result of an economic analysis with the assumption that variables will remain stable at the time the analysis is made. Nevertheless, CCHP systems by their nature work under uncertainties during their economic life. The proposed method has been tested with a representative case in this article. All the variables that affect the feasibility of the investment have been simulated with the non-parametric technique with the assumption that all the variables change as per normal distribution. In addition, the impact of the variables on the objective function has been assessed with the local method of sensitivity analysis. In the light of these results, this study contributes to decision makers during the CCHP planning by providing a different point of view at the stage of design and economic analysis for systems with uncertainties.

Suggested Citation

  • Ersoz, Ibrahim & Colak, Uner, 2016. "Combined cooling, heat and power planning under uncertainty," Energy, Elsevier, vol. 109(C), pages 1016-1025.
    • Handle: RePEc:eee:energy:v:109:y:2016:i:c:p:1016-1025
    DOI: 10.1016/j.energy.2016.04.071
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    1. Carpaneto, Enrico & Chicco, Gianfranco & Mancarella, Pierluigi & Russo, Angela, 2011. "Cogeneration planning under uncertainty: Part I: Multiple time frame approach," Applied Energy, Elsevier, vol. 88(4), pages 1059-1067, April.
    2. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    3. Tian, Zhe & Niu, Jide & Lu, Yakai & He, Shunming & Tian, Xue, 2016. "The improvement of a simulation model for a distributed CCHP system and its influence on optimal operation cost and strategy," Applied Energy, Elsevier, vol. 165(C), pages 430-444.
    4. Al-Mansour, Fouad & Kožuh, Mitja, 2007. "Risk analysis for CHP decision making within the conditions of an open electricity market," Energy, Elsevier, vol. 32(10), pages 1905-1916.
    5. 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.
    6. Carpaneto, Enrico & Chicco, Gianfranco & Mancarella, Pierluigi & Russo, Angela, 2011. "Cogeneration planning under uncertainty. Part II: Decision theory-based assessment of planning alternatives," Applied Energy, Elsevier, vol. 88(4), pages 1075-1083, April.
    7. Jradi, M. & Riffat, S., 2014. "Tri-generation systems: Energy policies, prime movers, cooling technologies, configurations and operation strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 396-415.
    8. Heiselberg, Per & Brohus, Henrik & Hesselholt, Allan & Rasmussen, Henrik & Seinre, Erkki & Thomas, Sara, 2009. "Application of sensitivity analysis in design of sustainable buildings," Renewable Energy, Elsevier, vol. 34(9), pages 2030-2036.
    9. Bischi, Aldo & Taccari, Leonardo & Martelli, Emanuele & Amaldi, Edoardo & Manzolini, Giampaolo & Silva, Paolo & Campanari, Stefano & Macchi, Ennio, 2014. "A detailed MILP optimization model for combined cooling, heat and power system operation planning," Energy, Elsevier, vol. 74(C), pages 12-26.
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