We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power scenarios by sampling from a conditional distribution that uses wind speed forecasts to tailor the scenarios to a specific day. We apply the generated scenarios in a stochastic day-ahead bidding problem of a wind electricity producer and analyze whether the scenarios yield profitable decisions. Compared to Gaussian copulas and Wasserstein-generative adversarial networks, the normalizing flow successfully narrows the range of scenarios around the daily trends while maintaining a diverse variety of possible realizations. In the stochastic day-ahead bidding problem, the conditional scenarios from all methods lead to significantly more stable profitable results compared to an unconditional selection of historical scenarios. The normalizing flow consistently obtains the highest profits, even for small sets scenarios.

我们提出了一种专门的场景生成方法，该方法利用预测信息为日前调度问题生成场景。特别是，我们通过从使用风速预测的条件分布中进行抽样来生成风电情景，从而将流量归一化以针对特定日期调整情景。我们将生成的场景应用于风力发电商的随机日前投标问题，并分析这些场景是否会产生有利可图的决策。与高斯 copula 和 Wasserstein 生成对抗网络相比，归一化流成功地缩小了围绕日常趋势的场景范围，同时保持了多种可能的实现。在随机日前出价问题中，与无条件选择历史情景相比，所有方法的有条件情景会导致明显更稳定的盈利结果。标准化流程始终获得最高的利润，即使对于小集合场景也是如此。