Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations

Prósper, Miguel A.; Otero-Casal, Carlos; Fernández, Francisco Santolaria; Míguez-Macho, Gonzalo

doi:10.1016/j.renene.2018.12.047

Cited by 104 publications

(41 citation statements)

References 24 publications

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“…RAW error results present a daily cycle, with values of the MAE around noon lower than during the night. These differences among the hourly WS MAEs are, however, not very significant, and the lower absolute errors at noon seem to be related with the better representation of turbulent fluxes during that part of the day [5]. In contrast, K-B 6 h errors do not follow a daily cycle, attaining values below 1.75 m/s mostly during daytime, but obtaining a worse result than RAW at 01 and 13 UTC.…”

Section: Wind Speed Nowcastingmentioning

confidence: 71%

“…The horizontal resolutions of D02, D03, and D04 are 3 km (121 × 121 grid points), 1 km (91 × 91 grid points) and 333 m (91 × 91 grid points) respectively. As demonstrated in Prósper et al [5], the high horizontal resolution used over this complex terrain increases the accuracy of the wind forecast. The innermost domain has 67 vertical levels, 7 of which lie within the first 200 m above ground, at about 14, 41, 70, 99, 127, 156 and 184 m, a distribution that better captures wind and temperature variations in the surface layer [42] and improves the performance of the wind turbine parameterization [11].…”

Section: Wind Farm Location and Wrf Configurationmentioning

confidence: 89%

“…A thorough validation of this model configuration for Coruxeiras wind farm and more information about the model physics options can be found in [5]. In the next table (Table 1), we present the main physics parameterizations used.…”

Section: Wind Farm Location and Wrf Configurationmentioning

confidence: 99%

“…The latter is where high-resolution meteorological modeling becomes increasingly prominent. Local short-term forecasts can supply valuable information in an operational wind farm, as wind speed and direction predictions can lead to a better running of the installation [4,5] or even provide alert for any extreme weather situation such as a downslope windstorm [6] or a severe frost [7].…”

Section: Introductionmentioning

confidence: 99%

“…The Weather Research and Forecasting (WRF) meso and microscale model has been used extensively during the last few years in the wind energy field for a variety of applications [8][9][10]. It includes a wind turbine parameterization [11] enabling the study of the wake effect from wind turbines [5,12]. However, Numerical Weather Prediction (NWP) simulations by themselves, present some limitations regarding subgrid scale phenomena forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

et al. 2019

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Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting (WRF) atmospheric model with an optimization obtained by the conjunction of a Kalman filter and a Bayesian model. This study focuses on the development and validation of this combined system in a very dense wind farm cluster located in Galicia (Northwest of Spain). A period of one year is simulated at 333 m horizontal resolution, with a daily operational forecasting set-up. The Kalman-Bayesian filter was tested both directly on wind speed and on the U-V (zonal and meridional) components for nowcasting periods from 10 min to 6 h periods, all of them with important applications in the wind industry. The results are quite promising, as the main statistical error indices are significantly improved in a 6 h forecasting horizon and even more in shorter horizon cases. The Mean Annual Error (MAE) for 1 h nowcasting horizon is 1.03 m/s for wind speed and 12.16 ° for wind direction. Moreover, the successful utilization of the integrated system in test cases with different characteristics demonstrates the potential utility that this tool may have for a variety of applications in wind farm operations and energy markets.

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Section: Wind Speed Nowcastingmentioning

confidence: 71%

Section: Wind Farm Location and Wrf Configurationmentioning

confidence: 89%

Section: Wind Farm Location and Wrf Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

et al. 2019

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Convection‐permitting modeling with regional climate models: Latest developments and next steps

Lucas‐Picher

Argüeso

Brisson

et al. 2021

WIREs Climate Change

133

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Approximately 10 years ago, convection-permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1-4 km) decadal-long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal ($1 km; $1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs.

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The effect of parked wind turbines on wind flow and turbulence over a complex terrain

2021

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Wind‐tunnel experiments were performed to study the wind characteristics on a parked wind turbine downwind of three types of hill and over a flat terrain. The focus of the study is on comparing wind characteristics on (a) a wind turbine standing alone and (b) this same type of wind turbine embedded in a wind farm. Particular emphasis is placed on the hill size and shape and the distance between the hill and the wind farm. The hill and wind‐farm models were subjected to an atmospheric boundary layer simulation to create realistic atmospheric conditions. Flow and turbulence were analyzed based on the measured mean flow velocity, Reynolds shear stress, turbulence intensity, and the power spectral density of velocity fluctuations. The experimental results reveal similar trends concerning (a) the wind characteristics obtained on a parked wind turbine embedded in a wind farm downwind of hills of various sizes and shapes and (b) the wind characteristics on this same type of parked wind turbine standing alone in the same position downwind of the same hills. In particular, the discrepancies in the mean flow velocity and turbulence intensity between these test cases are mostly below 5%, thus indicating that a complex terrain clearly has a dominant effect on the wind characteristics, while the effects of parked wind turbines on the wind characteristics are negligible. This important finding indicates that the structural loading of parked wind turbines situated on a complex terrain may be well calculated using the same procedures both for wind turbines standing alone and wind turbines embedded in wind farms if they are both placed at the same distance downwind of the same hills.

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Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations

Cited by 104 publications

References 24 publications

Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter

Convection‐permitting modeling with regional climate models: Latest developments and next steps

The effect of parked wind turbines on wind flow and turbulence over a complex terrain

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