The Influence of ENSO on the Long‐Term Water Storage Anomalies in the Middle‐Lower Reaches of the Yangtze River Basin: Evaluation and Analysis
Journal Article
Li, X., Jin, T., Liu, B., Chao, N., Li, F., & Cai, Z. (2023)
The Influence of ENSO on the Long‐Term Water Storage Anomalies in the Middle‐Lower Reaches of the Yangtze River Basin: Evaluation and Analysis. Earth and Space Science, 10(10), Article e2023EA003007. https://doi.org/10.1029/2023ea003007
Recent extreme events in the Middle‐Lower reaches of the Yangtze River basin (MLYRB) are proven to be possibly linked to the El Niño‐Southern Oscillation (ENSO) events as indi...
Numerical assessments of the effects of injected air temperature and well configuration on the cycle performance in compressed air energy storage in aquifers
Book Chapter
Yang, L., Cai, Z., Li, C., Guo, C., & He, Q. (2023)
Numerical assessments of the effects of injected air temperature and well configuration on the cycle performance in compressed air energy storage in aquifers. In J. Miocic, N. Heinemann, K. Edlmann, J. Alcalde, & R. Schultz (Eds.), Enabling Secure Subsurface Storage in Future Energy Systems. London: Geological Society. https://doi.org/10.1144/sp528-2022-74
Aquifers have advantages over salt caverns as the storage vessels for compressed air energy storage due to their wider availability for suitable sites and lower one-off capita...
Development of a Novel Simulator for Modelling Underground Hydrogen and Gas Mixture Storage
Journal Article
Cai, Z., Zhang, K., & Guo, C. (2022)
Development of a Novel Simulator for Modelling Underground Hydrogen and Gas Mixture Storage. International Journal of Hydrogen Energy, 47(14), 8929-8942. https://doi.org/10.1016/j.ijhydene.2021.12.224
Underground hydrogen storage can store grid-scale energy for balancing both short-term and long-term inter-seasonal supply and demand. However, there is no numerical simulator...
Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance
Journal Article
Fjelkestam Frederiksen, C. A., & Cai, Z. (2022)
Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance. Applied Energy, 306, Article 118152. https://doi.org/10.1016/j.apenergy.2021.118152
• Extra-terrestrial Solar Irradiance has been validated for PV output forecasting. • The machine learning approach successfully captures huge intra-daily PV output variations....