Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO136: High-Resolution Projections of Wind Energy Resources Under Climate Change in China
Jia Wu, Research Professor, National Climate Center, China Meteorological Administration
Abstract
Current global wind and solar resource projections predominantly utilize multi-model datasets such as CMIP6 and CORDEX, incorporating dynamical downscaling, statistical downscaling, and emerging AI-based techniques. However, these approaches present significant limitations for China's specific needs: CMIP6's coarse resolution (100-200 km) proves inadequate for detailed regional assessments, while CORDEX regional models (12-50 km) suffer from substantial data latency. This study pioneers the development of an autonomous, high-resolution projection system designed for operational applications in China's renewable energy sector. The research addresses three critical gaps: (1) The scarcity of high spatiotemporal resolution projection data; (2) The nascent stage of AI downscaling applications, particularly physics-informed machine learning techniques; (3) The absence of comprehensive risk assessments for future wind generation security. Our methodology advances regional climate modeling through enhanced driving fields and optimized physical parameterizations, introduces innovative dynamical-AI hybrid downscaling to improve projection accuracy for both inland and offshore renewable resources, and investigates mitigation strategies through multi-energy systems and cross-regional grid interconnections. We have developed comprehensive 18-km (national) and 5-km (provincial) resolution datasets by integrating regional climate modeling with advanced statistical downscaling techniques. Wind energy assessments employ rigorously bias-corrected ensemble simulations. The approach has been thoroughly validated against multi-source observational data across various emission scenarios.
No recording available for this poster.
