Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm

Yuan, Renyu; Wen-ju, JI; Luo, Kun; Wang, Jianwen; Zhang, Sanxia; Wang, Qiang; Fan, Jianren; Ni, Mingjiang; Cen, Kefa

doi:10.1016/j.apenergy.2017.08.018

Cited by 26 publications

(5 citation statements)

References 31 publications

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“…10), this raises the question of which upper limit of resolution is appropriate for applying WFPs. Different studies, conducted both onshore and offshore, found model results to be sensitive to both the horizontal and vertical resolution (Lee and Lundquist 2017b, a;Tomaszewski and Lundquist 2019;Siedersleben et al 2020;Pryor et al 2020), indicating that a horizontal resolution of at least 3-5 km is required to obtain reasonable results (Yuan et al 2017;Tomaszewski and Lundquist 2019;Pryor et al 2020;Siedersleben et al 2020). In addition, Pryor et al (2020) noted that simulated TKE values depend on the grid resolution and higher resolutions are associated with higher TKE values (Sect.…”

Section: Overview On Applications and Model Sensitivitiesmentioning

confidence: 99%

Review of Mesoscale Wind-Farm Parametrizations and Their Applications

Fischereit

Brown

Larsén

et al. 2021

Boundary-Layer Meteorol

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With the ongoing expansion of wind energy onshore and offshore, large-scale wind-farm-flow effects in a temporally- and spatially-heterogeneous atmosphere become increasingly relevant. Mesoscale models equipped with a wind-farm parametrization (WFP) can be used to study these effects. Here, we conduct a systematic literature review on the existing WFPs for mesoscale models, their applications and findings. In total, 10 different explicit WFPs have been identified. They differ in their description of the turbine-induced forces, and turbulence-kinetic-energy production. The WFPs have been validated for different target parameters through measurements and large-eddy simulations. The performance of the WFP depends considerably on the ability of the mesoscale model to simulate the background meteorological conditions correctly as well as on the model set-up. The different WFPs have been applied to both onshore and offshore environments around the world. Here, we summarize their findings regarding (1) the characterizations of wind-farm-flow effects, (2) the environmental impact of wind farms, and (3) the implication for wind-energy planning. Since wind-farm wakes can last for several tens of kilometres downstream depending on stability, surface roughness and terrain, neighbouring wind farms need to be taken into account for regional planning of wind energy. Their environmental impact is mostly confined to areas close to the farm. The review suggests future work should include benchmark-type validation studies with long-term measurements, further developments of mesoscale model physics and WFPs, and more interactions between the mesoscale and microscale community.

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Section: Overview On Applications and Model Sensitivitiesmentioning

confidence: 99%

Review of Mesoscale Wind-Farm Parametrizations and Their Applications

Fischereit

Brown

Larsén

et al. 2021

Boundary-Layer Meteorol

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“…The literature has shown that almost 20% efficiency can be extracted by employing a power curve calculated with the proper prediction of wind speed and power that is expected from the RMSE value compared to using only the manufacturer's power simulation result [103]. Hereafter, owing to the non-linearity of the curve power, at the wind-rated speed, where the slope between the speed wind of the activated turbine and the plateau is large, the wind speed prediction error will increase, and errors will be suppressed [91].…”

Section: Evaluation Of Wind Speed and Power Forecastsmentioning

confidence: 99%

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

et al. 2020

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To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

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“…It has been used for weather forecast, being officially adopted by the National Oceanic and Atmospheric Administration (NOAA), and for air quality modelling either when coupled to a chemistry model (Waked et al ., 2013) or when integrated with the additional WRF‐CHEM chemistry module (Grell et al ., 2011; Saide et al ., 2011). The WRF has been also used for various purposes, including wellness and health‐related applications (Doherty et al ., 2009), wind power potential mapping and forecast (Wharton et al ., 2013; Giannaros et al ., 2017), wind farm power output assessment (Yuan et al ., 2017), and as a forcing for Lagrangian particle dispersion models to simulate the atmospheric transport of passive scalars (Nehrkorn et al ., 2013) and particles (de Foy et al ., 2011; Bei et al ., 2013). All these applications are critically dependent on the capability of the WRF to simulate and reproduce the wind speed fields, among other variables, correctly.…”

Section: Introductionmentioning

confidence: 99%

WRF wind field assessment under multiple forcings using spatialized aircraft data

Carotenuto

Gualtieri

Toscano

et al. 2020

Meteorological Applications

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The performances of limited area weather models are affected by the choice of core solvers, domain resolutions, and initial and boundary conditions. To understand the extent of such differences on simulated wind fields, weather research and forecast (WRF) simulations initialized by different forcings were extensively compared with an aircraft-derived high-resolution data set. The two used forcings were the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis and the National Centers for Environmental Predictions (NCEP) Climate Forecast System Reanalysis (CFSR). The model domain covered a large portion of central western Italy (including part of the Tyrrhenian coast) encompassing the aircraft track and allowed the characterization of their performance across the simulation domain rather than a small set of point-based observations. The WRF results show good agreement with the aircraft data across the whole flight track with both forcings (root mean square errors (RMSEs) < 2.3 m•s −1 and an average r 2 = 0.7). Orography and coasts show an effect on simulated wind fields. The presence of a strong orography (which is smoothed by the model internal terrain elevation model) is associated with increased errors. Distance from the coast is also associated with a variation in RMSE (even if in a non-straightforward manner) because of potential breeze effects. No forcing data set clearly outperforms the other, while the ECMWF has higher correlation coefficients when considering wind direction.

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Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm

Cited by 26 publications

References 31 publications

Review of Mesoscale Wind-Farm Parametrizations and Their Applications

Review of Mesoscale Wind-Farm Parametrizations and Their Applications

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

WRF wind field assessment under multiple forcings using spatialized aircraft data

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