Quantum Renewable Scenario Generation
Zefan Tang, Peng Zhang, Yifan Zhou · 2022 IEEE Power & Energy Society General Meeting (PESGM) · 2022
This paper underpins the potential of quantum generative adversarial networks (QGANs) for renewable scenario generation in power grids. A single QGAN with either amplitude or angle encoding is hard to construct. To bridge the gaps, this paper devises a Multi-QGAN framework utilizing multiple QGANs. A correlation-based Multi-QGAN (CMulti-QGAN) approach is further established to improve the Multi-QGAN performance. Data from real solar systems in Connecticut are collected for numerical studies. Results demonstrate the effectiveness and robustness of Multi-QGAN and CMulti-QGAN, and also validate the superiority of CMulti-QGAN over Multi-QGAN.