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

TL;DR

This paper introduces a normalizing flow-based method for generating wind power scenarios using forecast data, improving day-ahead bidding profitability and reliability for wind farm operators.

Contribution

It develops a novel scenario generation approach with normalizing flows that outperforms Gaussian copulas and GANs in stability and profit in wind power scheduling.

Findings

Normalizing flows produce scenarios centered around daily trends with diverse realizations.

Conditional scenarios lead to more stable profits than historical scenarios.

Normalizing flows achieve the highest profits, especially with small scenario sets.

Abstract

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.