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1 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.
2 Nogues, Q.; Baulaz, Y.; Clavel, J.; Araignous, E.; Bourdaud, P.; Lasram, F. B. R.; Dauvin, J.-C.; Girardin, V.; Halouani, G.; Le Loc’h, F.; Loew-Turbout, F.; Raoux, A.; Niquil, N. 2023. The usefulness of food web models in the ecosystem services framework: quantifying, mapping, and linking services supply. Ecosystem Services, 63:101550. (Online first) [doi: https://doi.org/10.1016/j.ecoser.2023.101550]
Ecosystem services ; Food chains ; Models ; Mapping ; Ecosystem management ; Network analysis ; Climate change ; Wind farms ; Resilience ; Biodiversity ; Indicators ; Anthropogenic factors / France
(Location: IWMI HQ Call no: e-copy only Record No: H052205)
(8.03 MB)
Coastal ecosystems provide a wide range of valuable ecosystem services (ES) for human wellbeing. Such services depend on the functioning and structure of ecosystems. Unfortunately, these ecosystems are threatened by humans, directly impairing their ability to provide these services. In order to predict such changes, we used a food web model to forecast potential spatial changes in ES supply in the Seine Bay (English Channel), due to climate change effects (CC) and the setup of an offshore wind farm (OWF). Three ES were studied, food production from fishing, top predator production for cultural purposes and the potential resistance of the ecosystem inferred from its organization. The ability of the Seine Bay ecosystem to produce food appears to be negatively impacted by the effect of climate change. Because of the important economic role of fishing in Normandy, such changes could percolate on the entire social and economic system of the bay. The Courseulles-sur-Mer offshore wind farm appears to increase the supply of services and limit the impact of climate change at the local spatial scale, which could give stakeholders insights into mitigating the effects of climate change. Such ecosystem approach enables for a more integrative view of ES supply, through the characterization of the entire system functioning.
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