Combined methodology of statistical knowledge and adversarial learning for few-shot renewable scenario generation

Scenario generation plays a critical role in short-term power system operations with high renewable penetration. Data-driven scenario generation typically requires extensive sample data, however, due to confidentiality constraints or limited historical records—such as those associated with extreme weather scenarios—only small datasets may be available, thereby making credible scenario generation challenging. This paper proposes a combined methodology that integrates statistical knowledge and adversarial learning for few-shot renewable scenario generation. Specifically, the framework incorporates statistical knowledge that captures historical fluctuations and power prediction errors, together with conditional generative adversarial networks (CGANs), to generate accurate and reliable day-ahead or intraday look-ahead scenarios. This approach enables exploration of more diverse regions within the data space, generates a broader range of samples, and compensates for the lack of diversity resulting from limited datasets (e.g., one month or less). Case studies are conducted on a provincial power grid in China with abundant wind power resources. Compared with the traditional CGAN, the proposed methodology, when implemented with appropriate parameter settings, improves the coverage of the generated scenarios without increasing the corresponding power interval width.