论文标题
将基于流动的日常风力电力场景标准化，以供风电场运营商盈利和可靠的交付承诺
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators
论文作者
论文摘要
我们提出了一种专门的方案生成方法，该方法利用预测信息来生成用于日期调度问题的方案。特别是，我们使用归一化的流来通过从有条件的分布中取样，该分布使用风速预测来将场景定制为特定的一天。我们将生成的方案应用于风能生产者的随机日间招标问题中，并分析该方案是否产生有利可图的决策。与高斯Copulas和Wasserstein基因的对抗网络相比，正常化的流程成功地缩小了周围的每日趋势范围，同时保持了各种可能的实现。在随机日间招标问题中，与无条件选择的历史场景相比，所有方法的条件情况都会显着稳定的有利可图的结果。归一化流量始终获得最高利润，即使对于小型场景也是如此。
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.
