Wind Power Scenario Generation based on the Generalized Dynamic Factor Model and Generative Adversarial Network

• URL: http://arxiv.org/abs/2508.00692v1
• Date: Fri, 01 Aug 2025 15:08:03 GMT
• Title: Wind Power Scenario Generation based on the Generalized Dynamic Factor Model and Generative Adversarial Network
• Authors: Young-ho Cho, Hao Zhu, Duehee Lee, Ross Baldick,
• Abstract summary: We synthesize multiple long-term wind power scenarios of distributed wind farms simultaneously by using spatial and temporal correlation, waveforms, marginal ramp rates, and statistical characteristics.<n>To combine the advantages of GDFM and GAN, we use the GAN to provide a filter that extracts dynamic factors with temporal information from the observation data, and we then apply this filter in the GDFM to represent both spatial and frequency of plausible waveforms.
• Score: 6.5630456489767575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For conducting resource adequacy studies, we synthesize multiple long-term wind power scenarios of distributed wind farms simultaneously by using the spatio-temporal features: spatial and temporal correlation, waveforms, marginal and ramp rates distributions of waveform, power spectral densities, and statistical characteristics. Generating the spatial correlation in scenarios requires the design of common factors for neighboring wind farms and antithetical factors for distant wind farms. The generalized dynamic factor model (GDFM) can extract the common factors through cross spectral density analysis, but it cannot closely imitate waveforms. The GAN can synthesize plausible samples representing the temporal correlation by verifying samples through a fake sample discriminator. To combine the advantages of GDFM and GAN, we use the GAN to provide a filter that extracts dynamic factors with temporal information from the observation data, and we then apply this filter in the GDFM to represent both spatial and frequency correlations of plausible waveforms. Numerical tests on the combination of GDFM and GAN have demonstrated performance improvements over competing alternatives in synthesizing wind power scenarios from Australia, better realizing plausible statistical characteristics of actual wind power compared to alternatives such as the GDFM with a filter synthesized from distributions of actual dynamic filters and the GAN with direct synthesis without dynamic factors.

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