Your search found 3 records
1 Xu, Y.; Mo, X.; Cai, Y.; Li, X. 2005. Analysis on groundwater table drawdown by land use and the quest for sustainable water use in the Hebei Plain in China. Agricultural Water Management, 75(1):38-53.
(Location: IWMI-HQ Call no: PER Record No: H036920)
2 Cai, Y.; Breon, F.-M. 2021. Wind power potential and intermittency issues in the context of climate change. Energy Conversion and Management, 240:114276. (Online first) [doi: https://doi.org/10.1016/j.enconman.2021.114276]
Wind power ; Renewable energy ; Energy generation ; Electricity ; Climate change ; Wind farms ; Technology ; Wind speed ; Models ; Evaluation / France / Germany
(Location: IWMI HQ Call no: e-copy only Record No: H050420)
https://www.sciencedirect.com/science/article/pii/S0196890421004520/pdfft?md5=1cae745d768584e38659488011be79cd&pid=1-s2.0-S0196890421004520-main.pdf
https://vlibrary.iwmi.org/pdf/H050420.pdf
https://vlibrary.iwmi.org/pdf/H050420.pdf
(8.71 MB) (8.71 MB)
Wind power is developing rapidly because of its potential to provide renewable electricity and the large reduction in installation costs during the past decade. However, the high temporal variability of the wind power source is an obstacle to a high penetration in the electricity mix as it makes difficult to balance electricity supply and demand. There is therefore a need to quantify the variability of wind power and also to analyze how this variability decreases through spatial aggregation. In the context of climate change, it is also necessary to analyze how the wind power potential and its variability may change in the future. One difficulty for such objective is the large biases in the modeled winds, and the difficulty to derive a reliable power curve. In this paper, we propose an Empirical Parametric Power Curve Function (EPPCF) model to calibrate a power curve function for a realistic estimate of wind power from weather and climate model data at the regional or national scale. We use this model to analyze the wind power potential, with France as an example, considering the future wind turbine evolution, both onshore and offshore, with a focus on the production intermittency and the impact of spatial de-correlations. We also analyze the impact of climate change.
We show that the biases in the modeled wind vary from region to region, and must be corrected for a valid evaluation of the wind power potential. For onshore wind, we quantify the potential increase of the load factor linked to the wind turbine evolution (from a current 23% to 30% under optimistic hypothesis). For offshore, our estimate of the load factor is smaller for the French coast than is currently observed for installed wind farms that are further north (around 35% versus 39%). However, the estimates vary significantly with the atmospheric model used, with a large spatial gradient with the distance from the coast. The improvement potential appears smaller than over land. The temporal variability of wind power is large, with variations of 100% of the average within 3–10 h at the regional scale and 14 h at the national scale. A better spatial distribution of the wind farms could further reduce the temporal variability by around 20% at the national scale, although it would remain high with respect to that of the demand. The impact of climate change on the wind power resource is insignificant (from +2.7% to -8.4% for national annual mean load factor) and even its direction varies among models.
We show that the biases in the modeled wind vary from region to region, and must be corrected for a valid evaluation of the wind power potential. For onshore wind, we quantify the potential increase of the load factor linked to the wind turbine evolution (from a current 23% to 30% under optimistic hypothesis). For offshore, our estimate of the load factor is smaller for the French coast than is currently observed for installed wind farms that are further north (around 35% versus 39%). However, the estimates vary significantly with the atmospheric model used, with a large spatial gradient with the distance from the coast. The improvement potential appears smaller than over land. The temporal variability of wind power is large, with variations of 100% of the average within 3–10 h at the regional scale and 14 h at the national scale. A better spatial distribution of the wind farms could further reduce the temporal variability by around 20% at the national scale, although it would remain high with respect to that of the demand. The impact of climate change on the wind power resource is insignificant (from +2.7% to -8.4% for national annual mean load factor) and even its direction varies among models.
3 Lin, J.; Bryan, B. A.; Zhou, X.; Lin, P.; Do, H. X.; Gao, L.; Gu, X.; Liu, Z.; Wan, L.; Tong, S.; Huang, J.; Wang, Q.; Zhang, Y.; Gao, H.; Yin, J.; Chen, Z.; Duan, W.; Xie, Z.; Cui, T.; Liu, J.; Li, M.; Li, X.; Xu, Z.; Guo, F.; Shu, L.; Li, B.; Zhang, J.; Zhang, P.; Fan, B.; Wang, Y.; Zhang, Y.; Huang, J.; Li, X.; Cai, Y.; Yang, Z. 2023. Making China’s water data accessible, usable and shareable. Nature Water, 1:328-335. [doi: https://doi.org/10.1038/s44221-023-00039-y]
Water resources ; Data collection ; Databases ; Monitoring ; Modelling ; Water quality ; Wastewater treatment ; Stream flow ; Transboundary waters ; Water demand ; Infrastructure ; Policies / China
(Location: IWMI HQ Call no: e-copy only Record No: H052133)
(1.42 MB)
Water data are essential for monitoring, managing, modelling and projecting water resources. Yet despite such data—including water quantity, quality, demand and ecology—being extensively collected in China, it remains difficult to access, use and share them. These challenges have led to poor data quality, duplication of effort and wasting of resources, limiting their utility for supporting decision-making in water resources policy and management. In this Perspective we discuss the current state of China’s water data collection, governance and sharing, the barriers to open-access water data and its impacts, and outline a path to establishing a national water data infrastructure to reform water resource management in China and support global water-data sharing initiatives.
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