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The drivers of power system emissions: an econometric analysis of load, wind and forecast errors

机译:电力系统排放的驱动因素:负荷,风和预测误差的计量经济学分析

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摘要

This research models the drivers of emissions historically to identify the factors most effective in reducing power system emissions. It estimates the average effects of wind and load on $$hbox {CO}_{2}$$ CO 2 emissions from the Republic of Ireland’s electricity market. The findings suggest that wind generation and load reduction are not equally effective on average in terms of reducing emissions and that a 1 MW increase in wind is approximately 65% on average as effective at reducing emissions as a 1 MW load reduction, a result in line with existing literature. However, the results also show that a reduction in load and an increase in wind have a similar impact on emissions if wind forecast errors are explicitly modelled. Thus, the emissions reduction differentiation may not only be driven by the timing of load and wind output, the wind forecast error also has an important role. Positive and negative wind forecast errors are found to have opposite effects on emissions.
机译:这项研究从历史上模拟了排放的驱动因素,以找出最有效减少电力系统排放的因素。它估计了风和负荷对爱尔兰共和国电力市场$$ hbox {CO} _ {2} $$ CO 2排放量的平均影响。研究结果表明,就减少排放而言,风力发电和减少负荷的平均效果并不相同,并且每增加1兆瓦的风能与减少1兆瓦的负荷相比,平均减少减排的有效率约为65%,这表明现有文献。但是,结果还表明,如果明确模拟了天气预报误差,则负荷的减少和风的增加对排放也会产生类似的影响。因此,减排量的差异化不仅可以由负荷和风的输出时间决定,风的预测误差也起着重要的作用。发现正向和负向天气预报误差对排放有相反的影响。

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