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Integrating wave energy into the power grid: Simulation and forecasting

机译：将波能整合到电网中：仿真和预测

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A major issue in integrating renewable energy into power grids is short-term forecasting. If some share of electric power is derived from renewable sources, gaps between demand and supply must be made up by other forms of generation. Because of the uniquely short-lived nature of electricity, utilities need to be able to forecast over horizons of a few hours. Up to now, studies of wave energy have relied primarily on the flux, due to the unavailability of data on power flows. This study analyzes the power output using simulations. Five types of converters are simulated. The Pelamis P2 is currently in operation. The other four are currently under development: the two-body heaving converter, the heave buoy array, the three-body oscillating flap device, and the oscillating water column. Fluxes and power output series are calculated for three sites in the Pacific Northwest. Two sets of forecasting experiments are run for the fluxes and the power series. The first uses time series models-regressions and neural networks. The second uses a large-scale physics model, WAVEWATCH Ⅲ. The time series models forecast more accurately over short horizons, but the error increases rapidly as the horizon extends. The physics model generates similar degrees of accuracy over a range of horizons. The convergence point, at which time series and physics models yield similar degrees of accuracy, is in the range of 8-11 h. A third set of forecasting experiments is run for the gap between the demand for power and the supply from waves. The optimal means of predicting the gap is to run separate forecasts for demand and supply, and take the difference. The forecast error is lower than when the gap is forecasted directly.
机译：将可再生能源整合到电网中的主要问题是短期预测。如果一定比例的电力来自可再生能源，则供需之间的差距必须由其他形式的发电来弥补。由于电力具有短暂的短暂寿命，因此公用事业需要能够在几个小时的时间内进行预测。到目前为止，由于没有有关潮流的数据，对波能的研究主要依赖于通量。本研究使用仿真分析了功率输出。模拟了五种类型的转换器。 Pelamis P2当前正在运行。其他四个正在开发中：两体升沉转换器，升沉浮标阵列，三体摆动襟翼装置和摆动水柱。计算西北太平洋三个地点的通量和功率输出序列。对通量和幂级数进行了两组预测实验。第一种使用时间序列模型-回归和神经网络。第二种使用大型物理模型WAVEWATCHⅢ。时间序列模型可以在较短的时间范围内更准确地进行预测，但是随着时间范围的延伸，误差会迅速增加。物理模型在一定范围内会产生相似的准确度。时间序列和物理模型产生相似程度的准确度的收敛点在8-11小时范围内。针对电力需求和波浪供应之间的差距进行了第三组预测实验。预测缺口的最佳方法是对需求和供应进行单独的预测，并采用差异。预测误差低于直接预测差距时的误差。

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