Wave energy resource assessment
Wave energy resource assessment
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波浪能资源评估
DOI：
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发表时间：
2009
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通讯作者：
E. Mackay
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作者：
E. Mackay
使用卫星高度计数据的空间映射的波资源进行检查。开发并验证了一种根据高度计数据估计波浪周期的新算法，该算法可以推导出波浪能转换器（WEC）功率的估计值。从高度计数据得到的长期平均WEC功率图的空间分辨率高于从全球波浪模型数据得到的空间分辨率。它们可用于确定有前途的波能位置沿着特定的海岸线延伸，然后进行详细的研究，使用近岸模型。被认为是从波模型数据的WEC功率估计的准确性。在没有校准的情况下，来自模型数据的平均WEC功率估计可能会有10- 20%的偏差。波浪模型数据的校准由于模型参数对多个因素的非线性依赖以及偏差的季节性和年际变化而变得复杂。校准后，在一个网站的历史发电量的估计的准确性是5%的顺序，但不断变化的偏见，使其难以更精确地指定的准确性。WEC未来能源产量预测的准确性受到历史数据准确性和资源可变性的限制。在5，10和20年的平均功率水平的变异性进行了研究，在苏格兰北部的一个地区，并示出大于如果年度功率异常不相关的噪音。WEC发电对气候变化的敏感性也进行了研究，结果表明，在波浪农场的生命周期内，波浪气候的变化与自然水平的变化相比可能很小。结果表明，尽管波动气候的变化相关的不确定性，在历史数据的准确性的提高将提高未来WEC产量的预测的准确性。还考虑了极端波分析的主题。广义帕累托分布（GPD）的估计的比较。建议似然矩估计量应优先于其他估计量用于GPD。还考虑了极端情况季节模型的使用。在以往的研究中所作的断言相反，它表明，非季节性模型有一个较低的偏差和方差比模型，在不同的季节分析的数据。
The use of satellite altimeter data for spatial mapping of the wave resource is examined. A new algorithm for estimating wave period from altimeter data is developed and validated, which enables estimates of wave energy converter (WEC) power to be derived. Maps of the long-term mean WEC power from altimeter data are of a higher spatial resolution than is available from global wave model data. They can be used for identifying promising wave energy locations along particular stretches of coastline, before a detailed study using nearshore models is undertaken. The accuracy of estimates of WEC power from wave model data is considered. Without calibration estimates of the mean WEC power from model data can be biased of the order of 10-20%. The calibration of wave model data is complicated by non-linear dependence of model parameters on multiple factors, and seasonal and interannual changes in biases. After calibration the accuracy in the estimate of the historic power production at a site is of the order of 5%, but the changing biases make it difficult to specify the accuracy more precisely. The accuracy of predictions of the future energy yield from a WEC is limited by the accuracy of the historic data and the variability in the resource. The variability in 5, 10 and 20 year mean power levels is studied for an area in the north of Scotland, and shown to be greater than if annual power anomalies were uncorrelated noise. The sensitivity of WEC power production to climate change is also examined, and it is shown that the change in wave climate over the life time of a wave farm is likely to be small in comparison to the natural level of variability. It is shown that despite the uncertainty related to variability in the wave climate, improvements in the accuracy of historic data will improve the accuracy of predictions of future WEC yield. The topic of extreme wave analysis is also considered. A comparison of estimators for the generalised Pareto distribution (GPD) is presented. It is recommended that the Likelihood-Moment estimator should be used in preference to other estimators for the GPD. The use of seasonal models for extremes is also considered. In contrast to assertions made in previous studies, it is demonstrated that non-seasonal models have a lower bias and variance than models which analyse the data in separate seasons.