دانلود رایگان مقاله توزیع قیمت تراز کمی در تجزیه و تحلیل فنی- اقتصادی تصادفی

Abstract

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.

4. Conclusions

Breakeven price is an advantageous economic indicator compared with net present value (NPV) or internal rate of return (IRR) when evaluating emerging technologies with technoeconomic analysis (TEA). This study highlighted the stochastic techno-economic analysis in which Monte Carlo simulation was incorporated into traditional TEA. A case of cellulosic biofuel production from fast pyrolysis and hydroprocessing (FPH) pathway was used to illustrate the methodologies for quantifying the breakeven price distributions. A mathematical method and a programming method were developed to quantify breakeven price distribution in a way that can consider future price trend and uncertainty. It demonstrated that the breakeven price distributions derived using methods developed were coherent with the corresponding NPV distributions regarding the percentile value and the probability of gain/loss. The statistic sensitivity analysis by applying simulation data also provided useful implications. The stochastic TEA and the methods of creating breakeven price distribution can be applied to evaluating other technologies. Our experience suggests that breakeven price distributions communicate to investors and decision makers much more effectively than the typical NPV or IRR distributions, and users of the analysis can easily understand it. The distributions can be also used to conduct stochastic dominance analysis to compare projects from the perspective of risk-averse investors [20]. In addition, breakeven price distributions are useful for conducting policy analysis. One illustration is examining the impact of length of offtake contracts on likely bid price in a reverse auction. A reverse auction is one in which the lowest qualified bidder for an offtake contract wins the contract. Reverse auctions are one policy option being considered for government procurement of advanced biofuels. In prior work, it is found that the expected bid level decreases with the length of the contract because shorter contracts leave the bidder open to market price uncertainties for a longer period [13]. The study could have been considerably simplified if breakeven price distributions were applied. Furthermore, the scope of breakeven distribution can be broaden to any uncertain factors for the purpose of analysis, such as deriving a breakeven carbon tax distribution in a carbon mitigation policy analysis and breakeven land use change emission distribution in a life-cycle analysis.