How does El Niño Southern Oscillation impact the wind resource in Chile? A techno-economical assessment of the influence of El Niño and La Niña on the wind power

Watts, David; Durán, Pablo; Flores, Yarela

doi:10.1016/j.renene.2016.10.031

Cited by 21 publications

(8 citation statements)

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“…Monthly to seasonal forecasts are also increasingly available to inform planning for WT and grid maintenance (Yu et al, 2015;Torralba et al, 2017). Variability on intra-annual to decadal timescales (Pryor and Barthelmie, 2011;Pryor et al, 2006) arises primarily due to the action of internal climate modes such as the El Niño-Southern Oscillation (ENSO) (Schoof and Pryor, 2014;Pryor and Ledolter, 2010;Kirchner-Bossi et al, 2015;Bett et al, 2017;Watts et al, 2017) and climate nonstationarity (e.g., climate change due to the rising concentration of heattrapping gases) (Pryor and Barthelmie, 2010;Pryor et al, 2012a, b;Tobin et al, 2016) and is also key to dictating the electricity produced by WT arrays over their lifetime.…”

Section: Introductionmentioning

confidence: 99%

Interannual variability of wind climates and wind turbine annual energy production

2018

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Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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Section: Introductionmentioning

confidence: 99%

Interannual variability of wind climates and wind turbine annual energy production

2018

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Section: Introductionmentioning

confidence: 73%

“…Recent findings have showed that wind speed and energy production are highly influenced by El Niño and La Niña events [12]. The study found that the wind speed and energy produced decreases during El Niño events and increases during La Niña events [12]. The impacts of ENSO were also studied by [13] and they concluded that the ENSO will cause changes in the statistical characteristics of the wind resource during the lifetime of a project.…”

Section: Introductionmentioning

confidence: 97%

Influence of the ENSO and Monsoonal Season on Long-Term Wind Energy Potential in Malaysia

2018

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This paper assesses the long-term wind energy potential at three selected sites, namely Mersing and Kijal on the east coast of peninsular Malaysia and Kudat in Sabah. The influence of the El Niño-Southern Oscillation on reanalysis and meteorological wind data was assessed using the dimensionless median absolute deviation and wavelet coherency analysis. It was found that the wind strength increases during La Niña events and decreases during El Niño events. Linear sectoral regression was used to predict the long-term wind speed based on the 35 years of extended Climate Forecast System Reanalysis data and 10 years of meteorological wind data. The long-term monthly energy production was computed based on the 1.5 MW Goldwind wind turbine power curve. The measured wind data were extrapolated to the selected wind turbine default hub height (70 m.a.s.l) by using the site-specific power law indexed. The results showed that the capacity factor is higher during the Northeast monsoon (21.32%) compared to the Southwest monsoon season (3.71%) in Mersing. Moreover, the capacity factor in Kijal is also higher during the Northeast monsoon (10.66%) than during the Southwest monsoon (5.19%). However, in Kudat the capacity factor during the Southwest monsoon (36.42%) is higher compared to the Northeast monsoon (24.61%). This is due to the tail-effect of tropical storms that occur during this season in the South China Sea and Pacific Ocean.

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“…A method to estimate wind potential from observations and a diagnostic model were proposed by Morales et al 26 in south‐central Chile (34° –36° S). The impact of ENSO on wind power production in the Coquimbo region was studied by Watts et al, 27 whereas the capacity to generate off‐shore wind power potential in south‐central Chile was first estimated by Mattar and Borvarán 28 using the WRF model and in situ observations. Recently, two studies performed sensitivity tests to PBL schemes and other options in Chile 29,30 .…”

Section: Introductionmentioning

confidence: 99%

Improving wind speed forecasts from the Weather Research and Forecasting model at a wind farm in the semiarid Coquimbo region in central Chile

et al. 2020

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Accurate predictions of the wind field are key for better wind power forecasts. Wind speed forecasts from numerical weather models present differences with observations, especially in places with complex topography, such as the north of Chile. The present study has two goals: (a) to find the WRF model boundary layer (PBL) scheme that best reproduces the observations at the Totoral Wind Farm, located in the semiarid Coquimbo region in north‐central Chile, and (b) to use an artificial neural network (ANN) to postprocess wind speed forecasts from different model domains to analyze the sensitivity to horizontal resolution. The WRF model was run with three different PBL schemes (MYNN, MYNN3, and QNSE) for 2013. The WRF simulation with the QNSE scheme showed the best agreement with observations at the wind farm, and its outputs were postprocessed using two ANNs with two algorithms: backpropagation (BP) and particle swarm optimization (PSO). These two ANNs were applied to the innermost WRF domains with 3‐km (d03) and 1‐km (d04) horizontal resolutions. The root‐mean‐square errors (RMSEs) between raw WRF forecasts and observations for d03 and d04 were 2.7 and 2.4 ms−1, respectively. When both ANN models (BP and PSO) were applied to Domains d03 and d04, the RMSE decreased to values lower than 1.7 ms−1, and they showed similar performances, supporting the use of an ANN to postprocess a three‐nested WRF domain configuration to provide more accurate forecasts in advance for the region.

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How does El Niño Southern Oscillation impact the wind resource in Chile? A techno-economical assessment of the influence of El Niño and La Niña on the wind power

Cited by 21 publications

References 50 publications

Interannual variability of wind climates and wind turbine annual energy production

Interannual variability of wind climates and wind turbine annual energy production

Influence of the ENSO and Monsoonal Season on Long-Term Wind Energy Potential in Malaysia

Improving wind speed forecasts from the Weather Research and Forecasting model at a wind farm in the semiarid Coquimbo region in central Chile

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