Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators

• URL: http://arxiv.org/abs/2204.02242v1
• Date: Tue, 5 Apr 2022 14:27:25 GMT
• Title: Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators
• Authors: Eike Cramer, Leonard Paeleke, Alexander Mitsos, Manuel Dahmen
• Abstract summary: We present a specialized scenario generation that utilizes forecast information to generate scenarios for the particular usage in day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power generation scenarios by sampling from a conditional distribution that uses day-ahead wind speed forecasts to tailor the scenarios to the specific day. We apply the generated scenarios in a simple day-ahead bidding problem of a wind electricity producer and run a statistical analysis focusing on whether the scenarios yield profitable and reliable decisions.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a specialized scenario generation method that utilizes forecast information to generate scenarios for the particular usage in day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power generation scenarios by sampling from a conditional distribution that uses day-ahead wind speed forecasts to tailor the scenarios to the specific day. We apply the generated scenarios in a simple stochastic day-ahead bidding problem of a wind electricity producer and run a statistical analysis focusing on whether the scenarios yield profitable and reliable decisions. Compared to conditional scenarios generated from Gaussian copulas and Wasserstein-generative adversarial networks, the normalizing flow scenarios identify the daily trends more accurately and with a lower spread while maintaining a diverse variety. In the stochastic day-ahead bidding problem, the conditional scenarios from all methods lead to significantly more profitable and reliable results compared to an unconditional selection of historical scenarios. The obtained profits using the normalizing flow scenarios are consistently closest to the perfect foresight solution, in particular, for small sets of only five scenarios.

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