A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Changes in photovoltaic power output variability due to climate change in China: A multi-model ensemble mean analysis
Solar photovoltaic (PV) power plays a crucial role in mitigating climate change. However, climate change may amplify weather variability and extreme conditions. The extreme conditions can increase the very low PV output and thereby increase the need for grid stabilization services. This study examined how weather variability affects PV power output in the near- (2025–2054) and far-future (2071–2100). The ensemble mean calculated using seven global climate models participating in the coupled model intercomparison project phase 6 for three different shared socioeconomic pathways (SSPs) (SSP126, SSP245, SSP585) was used for the assessment. The standard deviation of the monthly PV power output and the share of very low monthly PV power output were used to assess the variability of PV power output. The findings indicate that the summer PV power output was projected to decrease by 6%–8% in central and northern Tibet under a high emissions scenario (SSP585). The summer months with low PV power output were projected to increase in western regions of China, known for its abundant solar resources. The findings of this study provide valuable insight for energy planners to make up for the influence of future weather variability.
Changes in photovoltaic power output variability due to climate change in China: A multi-model ensemble mean analysis
Solar photovoltaic (PV) power plays a crucial role in mitigating climate change. However, climate change may amplify weather variability and extreme conditions. The extreme conditions can increase the very low PV output and thereby increase the need for grid stabilization services. This study examined how weather variability affects PV power output in the near- (2025–2054) and far-future (2071–2100). The ensemble mean calculated using seven global climate models participating in the coupled model intercomparison project phase 6 for three different shared socioeconomic pathways (SSPs) (SSP126, SSP245, SSP585) was used for the assessment. The standard deviation of the monthly PV power output and the share of very low monthly PV power output were used to assess the variability of PV power output. The findings indicate that the summer PV power output was projected to decrease by 6%–8% in central and northern Tibet under a high emissions scenario (SSP585). The summer months with low PV power output were projected to increase in western regions of China, known for its abundant solar resources. The findings of this study provide valuable insight for energy planners to make up for the influence of future weather variability.
Changes in photovoltaic power output variability due to climate change in China: A multi-model ensemble mean analysis
Zuo, Hui-Min (author) / Lu, Hou-Liang (author) / Sun, Peng (author) / Qiu, Jun (author) / Li, Fang-Fang (author)
2024-03-01
13 pages
Article (Journal)
Electronic Resource
English
How Robust Is a Multi-Model Ensemble Mean of Conceptual Hydrological Models to Climate Change?
| DOAJ | 2022
Multi-Model Ensemble for day ahead prediction of photovoltaic power generation
| British Library Online Contents | 2016
Multi-Model Ensemble for day ahead prediction of photovoltaic power generation
| British Library Online Contents | 2016
Climate Change Impact Assessment on Korean Water Resources by Multi Model Ensemble
| British Library Conference Proceedings | 2009