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Wave Energy Converter Annual Energy Production Uncertainty Using Simulations

机译:使用模拟的波能转换器年发电量不确定性

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Critical to evaluating the economic viability of a wave energy project is: (1) a robust estimate of the electricity production throughout the project lifetime and (2) an understanding of the uncertainty associated with said estimate. Standardization efforts have established mean annual energy production (MAEP) as the metric for quantification of wave energy converter (WEC) electricity production and the performance matrix approach as the appropriate method for calculation. General acceptance of a method for calculating the MAEP uncertainty has not yet been achieved. Several authors have proposed methods based on the standard engineering approach to error propagation, however, a lack of available WEC deployment data has restricted testing of these methods. In this work the magnitude and sensitivity of MAEP uncertainty is investigated. The analysis is driven by data from simulated deployments of 2 WECs of different operating principle at 4 different locations. A Monte Carlo simulation approach is proposed for calculating the variability of MAEP estimates and is used to explore the sensitivity of the calculation. The uncertainty of MAEP ranged from 2%–20% of the mean value. Of the contributing uncertainties studied, the variability in the wave climate was found responsible for most of the uncertainty in MAEP. Uncertainty in MAEP differs considerably between WEC types and between deployment locations and is sensitive to the length of the input data-sets. This implies that if a certain maximum level of uncertainty in MAEP is targeted, the minimum required lengths of the input data-sets will be different for every WEC-location combination.
机译:评估波浪能项目的经济可行性的关键是:(1)在整个项目生命周期中对电力生产进行可靠的估算,以及(2)了解与所述估算相关的不确定性。标准化工作已经建立了年平均能源生产量(MAEP)作为量化波浪能转换器(WEC)发电量的指标,而性能矩阵法则是适当的计算方法。尚未获得用于计算MAEP不确定度的方法的普遍认可。几位作者提出了基于标准工程方法进行错误传播的方法,但是,缺乏可用的WEC部署数据限制了对这些方法的测试。在这项工作中,对MAEP不确定性的大小和敏感性进行了研究。该分析由来自在4个不同位置的2个不同操作原理的WEC的模拟部署中的数据驱动。提出了一种用于计算MAEP估计值变异性的Monte Carlo仿真方法,并用于探索计算的敏感性。 MAEP的不确定性范围为平均值的2%至20%。在所研究的不确定性中,发现波浪气候的变化是MAEP中大部分不确定性的原因。在WEC类型之间和部署位置之间,MAEP的不确定性差异很大,并且对输入数据集的长度很敏感。这意味着,如果以MAEP中某个最大不确定度为目标,则对于每个WEC位置组合,输入数据集的最小所需长度将有所不同。

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