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

Cramer, Eike; Dahmen, Manuel; Paeleke, Leonard; Mitsos, Alexander

doi:10.48550/ARXIV.2204.02242

Preprint

FZJ-2023-00759

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

Cramer, E.FZJ*RWTH* ; Paeleke, L.FZJ* ; Mitsos, A.FZJ*RWTH* ; Dahmen, M. (Corresponding author)FZJ*

2022
arXiv

arXiv (2022) [10.48550/ARXIV.2204.02242]

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Please use a persistent id in citations: http://hdl.handle.net/2128/33654 doi:10.48550/ARXIV.2204.02242

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.

Keyword(s): Optimization and Control (math.OC) ; Machine Learning (cs.LG) ; FOS: Mathematics ; FOS: Computer and information sciences

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