在Enel的决策过程中利用季节性预测的新方法

G. Piccioni, A. M. Nicolosi, M. Formenton, E. Musicò, Gloria Re, Andrea Mazzuoli, Martina Morgani, Elena Calcagni, Claudio Baldini
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引用次数: 1

摘要

在欧盟地平线2020 SECLIFIRM项目的框架内,Enel利用欧洲中期天气预报中心(ECMWF)的季节预报系统5 (SEAS5)来评估其对公司决策过程的附加值。评估是在Enel的国际领域中选择的五个案例研究中进行的。本文所述的工作展示了2015年至2016年期间意大利发生的极端事件的三个案例研究的最新进展,这些事件对能源行业造成了问题和可量化的影响。将空间聚合的SEAS5预报的概率信息与ECMWF ReAnalysis 5 (ERA5)天气模式相结合,得到一个包含2 m温度、总降水和10 m风速变量的临时数据集。然后将预测整合到Enel的模型中,然后通过一个性能指标来评估它们对最佳对冲策略的影响,该指标将获得的解决方案与气气学、季节预报和实际天气数据进行比较。第一步,通过误差分析来评估季节预报的质量。初步结果表明,季节5的温度值更接近ERA5气候。在风和总降水变量方面,ERA5气候学得到了改进。正如预期的那样,在更短的交货期内获得了SEAS5性能的逐步增强。该项目的进一步步骤将致力于将季节性预测应用于可再生电力生产和电力需求模型,以及对Enel决策过程的相对影响评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new approach to exploit seasonal forecasts in Enel’s decision-making process
In the frame of the EU Horizon 2020 SECLIFIRM project, Enel exploits the SEAsonal forecast System 5 (SEAS5) of the European Center for Medium- Range Weather Forecasts (ECMWF) to assess their added value on the company’s decision-making process. The assessment is performed on five case studies that were selected over Enel’s international domain. The work illustrated in this article shows the state of the art of three case studies focused on extreme events occurred in Italy between 2015 and 2016, that led to problematic and quantifiable impacts for the energy industry. The probabilistic information of spatially aggregated SEAS5 forecasts are combined with the ECMWF ReAnalysis 5 (ERA5) weather model to derive an ad-hoc dataset for variables of 2-m temperature, total precipitation, and 10-m wind speed. Forecasts are then integrated to Enel’s models, and their impact on the best hedging strategy is later evaluated through a performance indicator that compares solutions obtained with climatology, seasonal forecasts and actual weather data. As a first step, the quality of seasonal forecasts is assessed through an error analysis. Preliminary results on SEAS5 show that temperature values tend to fit ERA5 climatology. Improvements with respect to ERA5 climatology are observed for wind and total precipitation variables. As expected, a progressive enhancement of SEAS5 performance is obtained at shorter lead times. Further steps in the project will be dedicated to the application of seasonal forecasts to renewable electricity production and power demand models, and the relative impact assessment on Enel’s decision-making process.
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