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Stochastic Prediction of Offshore Wind Farm LCOE through an Integrated Cost Model

机译:综合成本模型海上风电场LCo的随机预测

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Common deterministic cost of energy models applied in offshore wind energy installations usually disregard the effect of uncertainty of key input variables-associated with OPEX, CAPEX, energy generation and other financial variables-on the calculation of levelized cost of electricity (LCOE). The present study aims at expanding a deterministic cost of energy model to systematically account for stochastic inputs. To this end, Monte Carlo simulations are performed to derive the joint probability distributions of LCOE, allowing for the estimation of probabilities of exceeding set thresholds of LCOE, determining certain confidence intervals. The results of this study stress the importance of appropriate statistical modelling of stochastic variables in order to reduce modelling uncertainties and contribute to a better informed decision making in renewable energy investments.
机译:在海上风能装置中应用的能量模型的常见确定性成本通常忽视关键输入变量的不确定性 - 与OPEX,CAPEX,能源生成和其他金融变量相关的影响 - 关于电力调用成本(LCoE)的计算。本研究旨在扩大能源模型的确定性成本,以系统地解释随机输入。为此,执行蒙特卡罗模拟以导出LCoE的联合概率分布,允许估计超过LCoE的设定阈值的概率,确定某些置信区间。该研究的结果强调了随机变量适当统计建模的重要性,以降低建模的不确定性,并有助于更好的可再生能源投资决策。

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