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Quantifying breakeven price distributions in stochastic techno-economic analysis


  • Zhao, Xin
  • Yao, Guolin
  • Tyner, Wallace E.


Techno-economic analysis (TEA) is a well-established modeling process for evaluating the economic feasibility of emerging technologies. Most previous TEA studies focused on creating reliable cost estimates but returned deterministic net present values (NPV) and deterministic breakeven prices which cannot convey the considerable uncertainties embedded in important techno-economic variables. This study employs stochastic techno-economic analysis in which Monte Carlo simulation is incorporated into traditional TEA. The distributions of NPV and breakeven price are obtained. A case of cellulosic biofuel production from fast pyrolysis and hydroprocessing pathway is used to illustrate the method of modeling stochastic TEA and quantifying the breakeven price distribution. The input uncertainties are translated to outputs so that the probability density distribution of both NPV and breakeven price are derived. Two methods, a mathematical method and a programming method, are developed to quantify breakeven price distribution in a way that can consider future price trend and uncertainty. Two scenarios are analyzed, one assuming constant real future output prices, and the other assuming that future prices follow an increasing trend with stochastic disturbances. It is demonstrated that the breakeven price distributions derived using the developed methods are consistent with the corresponding NPV distributions regarding the percentile value and the probability of gain/loss. The results demonstrate how breakeven price distributions communicate risks and uncertainties more effectively than NPV distributions. The stochastic TEA and the methods of creating breakeven price distribution can be applied to evaluating other technologies.

Suggested Citation

  • Zhao, Xin & Yao, Guolin & Tyner, Wallace E., 2016. "Quantifying breakeven price distributions in stochastic techno-economic analysis," Applied Energy, Elsevier, vol. 183(C), pages 318-326.
  • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:318-326
    DOI: 10.1016/j.apenergy.2016.08.184

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    References listed on IDEAS

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