With the increasing integration of wind and photovoltaic power in the whole European power system, there is a longing for detecting how to trade energy in the ever-changing intraday market from electric power industries. Intraday trading becomes even more relevant in the wake of the European Cross-Border Intraday (XBID) project, which aims at integrating electricity trading across Europe. Therefore, optimal trading strategies to address forecast fluctuations in renewables output are growingly required to be designed. In this study, we model, simulate and predict the evolution of wind and PV infeed forecasting errors over eight days preceding the start of a given quarter-hourly delivery period and updated in 15-min steps. We test comparatively the performance of several stochastic and probabilistic models, and recommend their complementary use, depending on the frequency in which forecast values are adjusted. Since ex-ante updated forecasting errors of renewables infeed are usually not available to researchers, simulations based on our proposed models break the ground for further applications to intraday pricing and optimization.

随着风电和光伏在整个欧洲电力系统中的日益融合，电力行业渴望在瞬息万变的盘中市场中探寻如何进行能源交易。在旨在整合整个欧洲的电力交易的欧洲跨境日内 (XBID) 项目之后，日内交易变得更加重要。因此，越来越需要设计最佳交易策略来解决可再生能源产量的预测波动。在这项研究中，我们建模、模拟和预测风能和光伏馈电预测误差在给定的每季度交付期开始前 8 天内的演变，并以 15 分钟的步长进行更新。我们比较测试了几种随机和概率模型的性能，并推荐它们的互补使用，取决于调整预测值的频率。由于研究人员通常无法获得可再生能源馈电的事前更新预测误差，因此基于我们提出的模型的模拟为进一步应用于日内定价和优化奠定了基础。