A Simple Temporal and Spatial Analysis of Flow in Complex Terrain in the Context of Wind Energy Modelling

Ayotte, Keith W.; Davy, Robert J.; Coppin, P. A.

doi:10.1023/a:1026583021740

Cited by 39 publications

(47 citation statements)

References 16 publications

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“…Meteorologists traditionally have focused wind forecasts at the 10-m level, a height at which official wind observations are routinely taken and a level at which winds are strongly influenced by surface friction. Prior wind forecasting research in the western United States has focused on flow in complex terrain (e.g., Wood 2000; Ayotte et al 2001) and is therefore not applicable in Iowa where boundary layer stratification, low-level jets (LLJs), and changing surface conditions are likely to be the dominant factors providing uncertainty in short-term forecasts at 80 m. Other modeling studies have taken a more statistical approach to predicting wind speed at different levels (Huang and Chalabi 1996); however, none have been reported for the state of Iowa, despite it being the state with the largest percentage of total power per capita coming from wind energy in 2010 (Department of Energy 2010). Even fewer studies have examined the forecasting of ramp events, defined as rapid changes in wind speed that lead to extreme changes in wind power output.…”

Section: Introductionmentioning

confidence: 99%

A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height

Deppe

Gallus

Takle

2013

Weather and Forecasting

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The Weather Research and Forecasting Model (WRF) with 10-km horizontal grid spacing was used to explore improvements in wind speed forecasts at a typical wind turbine hub height (80 m). An ensemble consisting of WRF model simulations with different planetary boundary layer (PBL) schemes showed little spread among the individual ensemble members for forecasting wind speed. A second configuration using three random perturbations of the Global Forecast System model produced more spread in the wind speed forecasts, but the ensemble mean possessed a higher mean absolute error (MAE). A third ensemble of different initialization times showed larger model spread, but model MAE was not compromised. In addition, postprocessing techniques such as training of the model for the day 2 forecast based on day 1 results and bias correction based on observed wind direction are examined. Ramp event forecasting was also explored. An event was considered to be a ramp event if the change in wind power was 50% or more of total capacity in either 4 or 2 h or less. This was approximated using a typical wind turbine power curve such that any wind speed increase or decrease of more than 3 m s21 within the 6-12 m s21 window (where power production varies greatly) in 4 h or less would be considered a ramp. Model MAE, climatology of ramp events, and causes were examined. All PBL schemes examined predicted fewer ramp events compared to the observations, and model forecasts for ramps in general were poor.Keywords global forecast system model, horizontal grid spacing, mean absolute error, planetary boundary layers, random perturbations, weather research and forecasting models, wind speed forecast, computer simulation, electric power transmission networks, wind effects, wind power, wind turbines ABSTRACTThe Weather Research and Forecasting Model (WRF) with 10-km horizontal grid spacing was used to explore improvements in wind speed forecasts at a typical wind turbine hub height (80 m). An ensemble consisting of WRF model simulations with different planetary boundary layer (PBL) schemes showed little spread among the individual ensemble members for forecasting wind speed. A second configuration using three random perturbations of the Global Forecast System model produced more spread in the wind speed forecasts, but the ensemble mean possessed a higher mean absolute error (MAE). A third ensemble of different initialization times showed larger model spread, but model MAE was not compromised. In addition, postprocessing techniques such as training of the model for the day 2 forecast based on day 1 results and bias correction based on observed wind direction are examined. Ramp event forecasting was also explored. An event was considered to be a ramp event if the change in wind power was 50% or more of total capacity in either 4 or 2 h or less. This was approximated using a typical wind turbine power curve such that any wind speed increase or decrease of more than 3 m s 21 within the 6-12 m s 21 window (where power production varies greatly) ...

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Section: Introductionmentioning

confidence: 99%

A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height

Deppe

Gallus

Takle

2013

Weather and Forecasting

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“…L'analyse de deux paramètres, c et k , qui caractérisent cette loi est à l'origine de nombreuses études [88], [89], [90], et [91]. En effet, ces deux paramètres qui dictent la manière 1) La courbe de puissance.…”

Section: Chapitre III Modelisation Et Dimensionnement D'un Système Hyunclassified

Étude et conception d'un générateur hybride d'électricité de type éolien-diesel avec élément de stockage d'air comprimé /

Ibrahim¹

2010

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RESUME ABSTRACTIn most remote areas, the diesel generator is the main source of electrical energy. For these regions, the price of extending the electricity grid is prohibitive and the fuel price increase dramatically with the remoteness. The continuie decline of renewable energy prices and their increased reliability led to greater use of renewable energy sources for the electrical generation in remote areas.In Quebec, in parallel with the rise of wind power, mainly in the form of large parks connected to the central grid of distribution, the power supply by diesel of the remote sites is still an immense technical and financial challenge. Indeed, this electricity production is relatively inefficient, expensive and responsible for the emission of large quantities of greenhouse gases (GHG). With the explosion of the fuel prices and high transport costs, the financial losses are colossal. These deficits, 133 million dollars per year according to HydroQuébec, reflect the difference between the high costs of local production of electricity in these regions and the uniform price of electricity.Paradoxically, most of these communities are situated in regions characterized by a sufficient wind resource for a commercial exploitation. The use of wind-diesel hybrid system (WDHS) in these autonomous networks could reduce the operating deficits. However, the profitability of the WDHS is reached at the condition of obtaining a high penetration rate of wind energy (PRWE) 3 which is possible only by using storage systems. After a thorough study based on a critical analysis of all the characteristics of the possible energy storage technologies, it was proposed a solution that meets all the technical and financial requirements while ensuring a reliable electricity supply of these sites. It is the wind-diesel hybrid system with compressed air energy storage (WDCAHS). This study led to the elaboration of 2 technical 3 PRWP -Penetration rate of wind power is the ratio between wind power and total power consumed by the load at the given moment PRWE -Penetration rate of wind energy is the ratio between the annual wind energy and the total annual energy consumed by the load The use of compressed air as the agent of energy storage is perfectly adapted as much to the wind production as to the diesels. In principle, the compressed air stored in the reservoirs during the periods of excess wind power production (strong wind) would be injected into the diesel generators in the case of the medium scale applications (villages, islands, ...) or in the compressed air engines for the small-scale applications (telecommunication stations, border posts, ...) and that during the periods of weak wind power production (low or no wind). This hybrid system would act in real time in order to maintain an optimal balance between the generated and the consumed powers and realize a remarkable decrease of the fuel consumption of diesel engine, whatever the level of the power demand.A modeling of each system was realized according to its nature of application....

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“…However, these studies are location specific (i.e. based on a single tall-tower meteorological weather station) and, hence, the revealed information is not representative for large areas (Ayotte, Davy, and Coppin 2001;Sempreviva, Barthelmie, and Pryor 2008;Newman and Klein 2014). A possible way to discover wind profile patterns with large coverage of spatial representation is using spatio-temporal pattern mining to capture frequent wind patterns continuously over time.…”

Section: Introductionmentioning

confidence: 99%

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining

Yusof

Zurita-Milla

2016

International Journal of Digital Earth

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Holistic understanding of wind behaviour over space, time and height is essential for harvesting wind energy application. This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining (MDSPM). This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded (0.125°× 0.125°) wind data for the Netherlands every 6 h and at six height levels. The wind data were first transformed into two spatio-temporal sequence databases (for speed and direction, respectively). Then, the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns, which were then visualized using a 3D wind rose, a circular histogram and a geographical map. These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines. Our analysis identified four frequent wind profile patterns. One of them highly suitable to harvest wind energy at a height of 128 m and 68.97% of the geographical area covered by this pattern already contains wind turbines. This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.ARTICLE HISTORY

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A Simple Temporal and Spatial Analysis of Flow in Complex Terrain in the Context of Wind Energy Modelling

Cited by 39 publications

References 16 publications

A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height

A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height

Étude et conception d'un générateur hybride d'électricité de type éolien-diesel avec élément de stockage d'air comprimé /

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining

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