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Simulating and forecasting ocean wave energy in western Canada

机译：模拟和预测加拿大西部的海浪能量

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While the technology now exists to harvest wave energy in coastal regions, the capital expenditures for wave farms can be substantial, so it is important to be able to simulate the power in advance. Further, to integrate wave energy into the grid, utilities need to forecast over short horizons and calculate reserve requirements. Wave farms are simulated at three locations in British Columbia, Canada. Power series are calculated for six types of wave energy converters (WECs), four that operate in deep water, and two in shallow water. Forecasts are run using a physics-based model and statistical models. Five major conclusions emerge from the analysis. First, given the intermittency of buoy data, physics model hindcasts are an effective method of interpolating missing values. Second, the power output from converters does not have the same properties as the wave energy flux. Instead, the power output is a nonlinear function of the wave height and period, with fewer large outliers. Third, time series models predict well over near-term horizons while physics models forecast more accurately over longer horizons. The convergence point, at which the two types of models achieve comparable degrees of accuracy, is in the area of 2-3 h in these data sets, lower than in most prior studies. The recommendation is to use time series methods to forecast at the horizons required for reserves, and physics models for long-term planning. Fourth, the predictability of the power output can differ substantially for individual converters. Finally, wave energy is found to be significantly less costly in terms of reserves than wind and solar. (C) 2015 Elsevier Ltd. All rights reserved.
机译：尽管现在已经有了在沿海地区收集波浪能的技术，但波浪农场的资本支出可能是巨大的，因此能够预先模拟功率非常重要。此外，为了将波浪能整合到电网中，公用事业需要在短时间内进行预测并计算储量要求。在加拿大不列颠哥伦比亚省的三个地点模拟了波浪场。计算了六种类型的波能转换器（WEC）的功率序列，其中四种在深水中运行，而两种在浅水中运行。预测是使用基于物理的模型和统计模型进行的。分析得出五个主要结论。首先，考虑到浮标数据的间歇性，物理模型后验是插值缺失值的有效方法。其次，转换器输出的功率与波能通量的特性不同。取而代之的是，功率输出是波高和周期的非线性函数，而大的离群值则更少。第三，时间序列模型可以在近期范围内进行很好的预测，而物理模型可以在更长的范围内进行更准确的预测。在这些数据集中，两种类型的模型达到的精确度相当的收敛点在2-3 h范围内，低于大多数以前的研究。建议使用时间序列方法来预测储量所需的时间范围，并使用物理模型进行长期规划。第四，对于各个转换器，功率输出的可预测性可能会大不相同。最后，发现波能的储藏成本比风能和太阳能便宜得多。 （C）2015 Elsevier Ltd.保留所有权利。

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