Operation-adversarial scenario generation

Zhirui Liang, Robert Mieth, Yury Dvorkin

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number108451
JournalElectric Power Systems Research
StatePublished - Nov 2022


  • Conditional generative adversarial network (cGAN)
  • DC optimal power flow (OPF)
  • Operation-adversarial learning

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Operation-adversarial scenario generation'. Together they form a unique fingerprint.

Cite this