Review of Wind Energy Forecasting Methods for Modeling Ramping Events (original) (raw)
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Weather and Forecasting, 2020
The second Wind Forecast Improvement Project (WFIP2) is a multiagency field campaign held in the Columbia Gorge area (October 2015–March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011–August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-heig...
2010
We evaluate forecasts made with the WRF ARW model run with seven different boundary layer (BL) parameterizations. The simulations are evaluated in terms of the best performance in wind energy forecasting, i.e. in forecasting winds at hub height as well as the correct shape of the wind shear. The model runs are short-term wind forecasts (0-30 hours) for October 2009 and are compared to measurements from the 116/160meter meteorological mast/light tower at the Risø National Test Station for Large Wind Turbines at Høvsøre, Denmark. When evaluating wind profiles, we compute the α-parameter, a measure for stability derived from the power law. The results show that the YSU BL scheme does not exhibit the desired variation in the wind profiles from stable to unstable in the course of a day and tends to nearly always produce vertical wind shear typical of the neutral atmosphere. The wind profiles forecast with WRF using the BL schemes based on turbulence kinetic energy, however, compare better with the observations. All the evaluated schemes tend to underestimate the wind at hub height during the night and overestimate it during the day. The diurnal evolution and the expected transitions of wind speed, temperature and the α-parameter are well captured by all of the schemes, except for the YSU scheme.
The impact of model physics on numerical wind forecasts
Renewable Energy, 2013
Fine scale numerical weather prediction (NWP) models are now widely applied to predict power production at wind farms. Given the fact that demand for specialized forecasts for wind farms is growing, it is important to understand the strengths and limitations of NWP models for producing wind forecasts. This paper seeks to partially fulfill this goal by exploring the sensitivity of NWP-based wind forecasts to the choice of model physics schemes. The authors used two distinct case studies to explore these sensitivities with a NWP model used in realtime wind power forecast, where the underlying meteorology in both cases had a profound impact on the wind ramp-up of a wind farm in Northern Colorado. The first case was a strong cold frontal system moving through the wind farm during winter, and the second case was for a line of strong thunderstorms passing through the wind farm during summer. The model results were compared with observed hub-height wind. In each case, sensitivity studies were conducted to explore the impact of the choice of physics schemes on the wind forecasts. For the winter case, the sensitivity to the representation of land surface and planetary boundary layer processes was quantified. For the summer case, the sensitivity to the representation of clouds and precipitation physics was explored. In the winter case, the wind forecast was less sensitive to the choice of physics schemes due to the hub-height winds being generated by strongly forced large-scale weather systems. In contrast, the wind forecast for the summer case (driven by weaker meteorological forcing) was strongly affected by the choice of cloud and precipitation physics schemes.
Improving Wind Energy Forecasting through Numerical Weather Prediction Model Development
Bulletin of the American Meteorological Society
The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed bi...
Atmosphere
The intermittent nature of wind resources is challenging for their integration into the electrical system. The identification of weather systems and the accurate forecast of wind ramps can improve wind-energy management. In this study, extreme wind ramps were characterized at four different geographical sites in terms of duration, persistence, and weather system. Mid-latitude systems are the main cause of wind ramps in Mexico during winter. The associated ramps last around 3 h, but intense winds are sustained for up to 40 h. Storms cause extreme wind ramps in summer due to the downdraft contribution to the wind gust. Those events last about 1 to 3 h. Dynamic downscaling is computationally costly, and statistical techniques can improve wind forecasting. Evaluation of the North American Mesoscale Forecast System (NAM) operational model to simulate wind ramps and two bias-correction methods (simple bias and quantile mapping) was done for two selected sites. The statistical adjustment r...
Predictability of Low-Level Winds by Mesoscale Meteorological Models
Monthly Weather Review, 2004
This study describes the verification of model-based, low-level wind forecasts for the area of the Salt Lake valley and surrounding mountains during the 2002 Salt Lake City, Utah, Winter Olympics. Standard verification statistics (such as bias and mean absolute error) for wind direction and speed were compared for four models: the Eta, Rapid Update Cycle (RUC-2), and Global Forecast System of the National Centers for Environmental Prediction, and the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5). Even though these models had horizontal grid increments that ranged over almost two orders of magnitude, the highestresolution MM5 with a 1.33-km grid increment exhibited a forecast performance similar to that of the other models in terms of grid-average, conventional verification metrics. This is in spite of the fact that the MM5 is the only model capable of reasonably representing the complex terrain of the Salt Lake City region that exerts a strong influence on the local circulation patterns. The purpose of this study is to investigate why the standard verification measures did not better discriminate among the models and to describe alternative measures that might better represent the ability of high-horizontal-resolution models to forecast locally forced mesogammascale circulations. The spatial variability of the strength of the diurnal forcing was quantified by spectrally transforming the time series of wind-component data for each observation location. The amount of spectral power in the band with approximately a diurnal period varied greatly from place to place, as did the amount of power in the bands with periods longer (superdiurnal) and shorter (subdiurnal) than the diurnal. It is reasonable that the superdiurnal power is largely in the synoptic-scale motions, and thus can be reasonably predicted by all the models. In contrast, the subdiurnal power is mainly in nondiurnally forced small-scale fluctuations that are generally unpredictable with any horizontal resolution because they are unobserved in three dimensions by the observation network.
Improving Wind-Ramp Forecasts in the Stable Boundary Layer
Boundary-Layer Meteorology, 2017
The viability of wind-energy generation is dependent on highly accurate numerical wind forecasts, which are impeded by inaccuracies in model representation of boundary-layer processes. This study revisits the basic theory of the Mellor, Yamada, Nakanishi, and Niino (MYNN) planetary boundary-layer parametrization scheme, focusing on the onset of windramp events related to nocturnal low-level jets. Modifications to the MYNN scheme include: (1) calculation of new closure parameters that determine the relative effects of turbulent energy production, dissipation, and redistribution; (2) enhanced mixing in the stable boundary layer when the mean wind speed exceeds a specified threshold; (3) explicit accounting of turbulent potential energy in the energy budget. A mesoscale model is used to generate shortterm (24 h) wind forecasts for a set of 15 cases from both the U.S.A. and Germany. Results show that the new set of closure parameters provides a marked forecast improvement only when used in conjunction with the new mixing length formulation and only for cases that are originally under-or over-forecast (10 of the 15 cases). For these cases, the mean absolute error (MAE) of wind forecasts at turbine-hub height is reduced on average by 17%. A reduction in MAE values on average by 26% is realized for these same cases when accounting for the turbulent potential energy together with the new mixing length. This last method results in an average reduction by at least 13% in MAE values across all 15 cases.
Geoscientific Model Development Discussions, 2019
During the second Wind Forecast Improvement Project (WFIP2; Oct 2015-Mar 2017, Columbia River Gorge and Basin area) several improvements to the parameterizations applied in the High Resolution Rapid Refresh (HRRR-3 km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST-750 m horizontal grid spacing) Numerical Weather Prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80-m wind speeds (hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and 3 profiling lidars for verification. Improvements due to the experimental physics (EXP vs CNT runs) versus those due to finer horizontal grid spacing (HRRRNEST vs HRRR), and the combination of the two are compared, using standard bulk statistics such as Mean Absolute Error (MAE) and Mean Bias Error (bias). On average, the HRRR 80-m wind speed MAE is reduced by 3-4% due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5% on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80-m wind speed MAE, up to 7-8%. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena, also looking for the causes of model weaknesses. Finally, bias correction methods are applied to the 80-m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors.
Journal of Applied Meteorology and Climatology, 2013
One challenge with wind-power forecasts is the accurate prediction of rapid changes in wind speed (ramps). To evaluate the Weather Research and Forecasting (WRF) model's ability to predict such events, model simulations, conducted over an area of complex terrain in May 2011, are used. The sensitivity of the model's performance to the choice among three planetary boundary layer (PBL) schemes [Mellor–Yamada–Janjić (MYJ), University of Washington (UW), and Yonsei University (YSU)] is investigated. The simulated near-hub-height winds (62 m), vertical wind speed profiles, and ramps are evaluated against measurements obtained from tower-mounted anemometers, a Doppler sodar, and a radar wind profiler deployed during the Columbia Basin Wind Energy Study (CBWES). The predicted winds at near–hub height have nonnegligible biases in monthly mean under stable conditions. Under stable conditions, the simulation with the UW scheme better predicts upward ramps and the MYJ scheme is the most...
An investigation of mesoscale wind direction changes and their consideration in engineering models
2022
We propose that considering mesoscale wind direction changes in the computation of wind farm cluster wakes could reduce the uncertainty of engineering wake modelling tools. The relevance of mesoscale wind direction changes is investigated using a wind climatology of the German Bight area covering 30 years, derived from the New European Wind Atlas (NEWA). Furthermore, we present a new solution for engineering modelling tools that accounts for the effect of such changes on the propagation of cluster wakes. Mesoscale wind direction changes are found to exceed 7 • per 100 km in 50 % of all cases and are particularly large in the lower partial load range, which is associated with strong wake formation. Here, the quartiles reach up to 20 • per 100 km. Especially on a horizontal scale of several tens to a hundred kilometers, wind direction changes are relevant. Both the temporal and spatial scale at which large wind direction changes occur depend on the presence of pressure systems. Furthermore, atmospheric conditions which promote far-reaching wakes were found to align with a strong turning in 14.6 % of the cases. In order to capture these mesoscale wind direction changes in engineering model tools, a wake propagation model was implemented into the Fraunhofer IWES wind farm and wake modelling software flappy. The propagation model derives streamlines from the horizontal velocity field and forces the single turbine wakes along these streamlines. This model has been qualitatively evaluated by simulating the flow around wind farm clusters in the German Bight with data from the mesoscale atlas of NEWA and comparing the results to Synthetic Aperture Radar (SAR) measurements for selected situations. The comparison reveals that the flow patterns are in good agreement if the underlying mesoscale data capture the velocity field well. For such cases, the new model provides an improvement compared to the baseline approach of engineering models, which assumes a straight-line propagation of wakes. The streamline and the baseline model have been further compared in terms of their quantitative effect on the energy yield. Simulating two neighbouring wind farm clusters over a time period of 10 years, it is found that there are no significant differences across the models when computing the total energy yield of both clusters. However, extracting the wake effect of one cluster on the other, the two models show a difference of about 1 %. Even greater differences are commonly observed when comparing single situations. Therefore, we claim that the model has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.