Operation-adversarial scenario generation

Journal Article · 20 July 2022 · Electric Power Systems Research

DOI: https://doi.org/10.1016/j.epsr.2022.108451 · OSTI ID:2422356

Liang, Zhirui ^[1]; Mieth, Robert ^[2]; Dvorkin, Yury ^[2]

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1. New York University (NYU), NY (United States); OSTI
2. New York University (NYU), NY (United States)

This paper proposes a modified conditional generative adversarial network (cGAN) model to generate net load scenarios for power systems that are statistically credible, conditioned by given labels (e.g., seasons), and, at the same time, “stressful” to the system operations and dispatch decisions. The measure of stress used in this paper is based on the operating cost increases due to net load changes. The proposed operation-adversarial cGAN (OA-cGAN) internalizes a DC optimal power flow model and seeks to maximize the operating cost and achieve a worst-case data generation. The training and testing stages employed in the proposed OA-cGAN use historical day-ahead net load forecast errors and has been implemented for the realistic NYISO 11-zone system. In conclusion, our numerical experiments demonstrate that the generated operation-adversarial forecast errors lead to more cost-effective and reliable dispatch decisions.

• Research Organization: Columbia University, New York, NY (United States)
• Sponsoring Organization: USDOE Advanced Research Projects Agency - Energy (ARPA-E)
• Grant/Contract Number: AR0001300
• OSTI ID: 2422356
• Journal Information: Electric Power Systems Research, Journal Name: Electric Power Systems Research Journal Issue: C Vol. 212; ISSN 0378-7796
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English