Operation-adversarial scenario generation

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. Our numerical experiments demonstrate that the generated operation-adversarial forecast errors lead to more cost-effective and reliable dispatch decisions. [Display omitted] •We propose a cGAN model to improve power system scenario generation.•We generate statistically credible data that is stressful to system operations.•We internalize a DC-OPF model in the training phase to inform worst-case error generation.•We test it on the NYISO 11-zone system to generate forecast errors of net load.•The proposed approach leads to cheaper dispatch decisions than robust benchmarks.