Wind energy forecasting can facilitate wind energy integration into a power system. In particular, the management of power system security would benefit from forecast information on plausible large, rapid change in wind power generation. Numerical Weather Prediction (NWP) systems are presently the best available tools for wind energy forecasting for projection times between 3 and 48 hours. In this thesis, the types of weather phenomena that cause large, rapid changes in wind power in southeast Australia are classified using observations from three wind farms. The results show that the majority of events are due to horizontal propagation of spatial weather features. A study of NWP systems reveals that they are generally good at forecasting the broad large-scale weather phenomena but may misplace their location relative to the physical world. Errors may result from developing single time-series forecasts from a single NWP grid point, or from a single interpolation of proximate grid points. This thesis presents a new approach that displays NWP wind forecast information from a field of multiple grid points around the wind farm location. Displaying the NWP wind speeds at the multiple grid points directly would potentially be misleading as they each reflect the estimated local surface roughness and terrain at a particular grid point. Thus, a methodology was developed to convert the NWP wind speeds at the multiple grid points to values that reflect surface conditions at the wind farm site. The conversion method is evaluated with encouraging results by visual inspection and by comparing with an NWP ensemble. The multiple grid point information can also be used to improve downscaling results by filtering out data where there is a large chance of a discrepancy between an NWP time-series forecast and observations. The converted wind speeds at multiple grid points can be downscaled to site-equivalent wind speeds and transformed to wind farm power assuming unconstrained wind farm operation at one or more wind farm sites. This provides a visual decision support tool that can help a forecast user assess the possibility of large, rapid changes in wind power from one or more wind farms.

The research presented herein concentrates on the quantification, assessment and forecasting of intra-hour wind power variability. Wind power is intrinsically variable and, due to the increase in wind power penetration levels, the level of intra-hour wind power variability is expected to increase as well. Existing metrics used in wind integration studies fail to efficiently capture intra-hour wind power variation. As a result, this can lead to an underestimation of intra-hour wind power variability with adverse effects on power systems, especially their reliability and economics. One major research focus in this dissertation is to develop a novel variability metric which can effectively quantify intra-hour wind power variability. The proposed metric, termed conditional range metric (CRM), quantifies wind power variability using the range of wind power output over a time period. The metric is termed conditional because the range of wind power output is conditioned on the time interval length k and on the wind power average production l[subscript j] over the given time interval. Using statistical analysis and optimization approaches, a computational algorithm to obtain a unique p[superscript th] quantile of the conditional range metric is given, turning the proposed conditional range metric into a probabilistic intra-hour wind power variability metric. The probabilistic conditional range metric CRM[subscript k,l subscript j,p] assists power system operators and wind farm owners in decision making under uncertainty, since decisions involving wind power variability can be made based on the willingness to accept a certain level of risk [alpha] = 1 - p. An extensive performance analysis of the conditional range metric on real-world wind power and wind speed data reveals how certain variables affect intra-hour wind power variability. Wind power variability over a time frame is found to increase with increasing time frame size and decreasing wind farm size, and is highest at mid production wind power levels. Moreover, wind turbines connected through converters to the grid exhibit lower wind power variability compared to same size simple induction generators, while wind power variability is also found to decrease slightly with increasing wind turbine size. These results can lead to improvements in existing or definitions of new wind power management techniques. Moreover, the comparison of the conditional range metric to the commonly used step-changes statistics reveals that, on average, the conditional range metric can accommodate intra-hour wind power variations for an additional 15% of hours within a given year, significantly benefiting power system reliability. The other major research focus in this dissertation is on providing intrahour wind power variability forecasts. Wind power variability forecasts use pth CRM quantiles estimates to construct probabilistic intervals within which future wind power output will lie, conditioned on the forecasted average wind power production. One static and two time-adaptive methods are used to obtain p[superscript th] CRM quantiles estimates. All methods produce quantile estimates of acceptable reliability, with average expected deviations from nominal proportions close to 1%. Wind power variability forecasts can serve as joint-chance constraints in stochastic optimization problems, which opens the door to numerous applications of the conditional range metric. A practical example application uses the conditional range metric to estimate the size of an energy storage system (ESS). Using a probabilistic forecast of wind power hourly averages and historical data on intra-hour wind power variability, the proposed methodology estimates the size of an ESS which minimizes deviations from the forecasted hourly average. The methodology is evaluated using real-world wind power data. When the estimated ESS capacities are compared to the ESS capacities obtained from the actual data, they exhibit coverage rates which are very close to the nominal ones, with an average absolute deviation less than 1.5%. / text

Recently is topicality making wherewith more electrical energy from “clean” stockholding. Detractive hothouse gas exhaustion to the atmosphere. One “clean” energy is – wind power. Wind plans working in the commensurable electrical energy supply system, must “nuisance��. For this purpose essential wind electricity work forecast and respectively react to the situation. Wind power – station made electrical energy forecasting analysis I done sustaining by California experience. Forecasting is executable by wind forecast ground. Wind forecasts possible get from meteorological dimensional station or other institutions which doing wind range. Wind power – stations work essential to forecast at one hour 48 hours to the future. Such period is needful to other power – stations reaction. Wind power – stations worst worth to be built in the seaside zone or in the local pelagic there are not high flora or buildings. Wind power – stations can be built and in the others regions, but then increase the period of dividend.

Wind energy forecasting can facilitate wind energy integration into a power system. In particular, the management of power system security would benefit from forecast information on plausible large, rapid change in wind power generation. Numerical Weather Prediction (NWP) systems are presently the best available tools for wind energy forecasting for projection times between 3 and 48 hours. In this thesis, the types of weather phenomena that cause large, rapid changes in wind power in southeast Australia are classified using observations from three wind farms. The results show that the majority of events are due to horizontal propagation of spatial weather features. A study of NWP systems reveals that they are generally good at forecasting the broad large-scale weather phenomena but may misplace their location relative to the physical world. Errors may result from developing single time-series forecasts from a single NWP grid point, or from a single interpolation of proximate grid points. This thesis presents a new approach that displays NWP wind forecast information from a field of multiple grid points around the wind farm location. Displaying the NWP wind speeds at the multiple grid points directly would potentially be misleading as they each reflect the estimated local surface roughness and terrain at a particular grid point. Thus, a methodology was developed to convert the NWP wind speeds at the multiple grid points to values that reflect surface conditions at the wind farm site. The conversion method is evaluated with encouraging results by visual inspection and by comparing with an NWP ensemble. The multiple grid point information can also be used to improve downscaling results by filtering out data where there is a large chance of a discrepancy between an NWP time-series forecast and observations. The converted wind speeds at multiple grid points can be downscaled to site-equivalent wind speeds and transformed to wind farm power assuming unconstrained wind farm operation at one or more wind farm sites. This provides a visual decision support tool that can help a forecast user assess the possibility of large, rapid changes in wind power from one or more wind farms.

When the wind blows and wind turbine generators harvests the kinetic energy and trans- forms it to electrical power, there is a need for predicting how much power that will be dispatched from the turbines. Even the most perfect computer model with high computa- tional power could not model the beauty of the forces of nature and we must accept some degree of forecasting error in the predicted power output due to the inherently stochastic patterns in the atmosphere.  This project set out to investigate the main reasons and factors that impacts the forecasting error related to wind power assets on Gotland. From theory and the performed case study, wind speed is the strongest predictor of wind power production, to claim anything else would be severely inaccurate. However, the main predictors of wind power prediction are summarized from a literature study, extracted from a weather model and tried in a case study for the wind farm Stugylparken on Näsudden, Gotland. Three different prediction methods were tried and the ensemble trees model was the best model by the evaluation metrics that was chosen. The second-best performing model was the artificial neural network, and prediction by theoretical power curve performed worse than the standard machine learning methods what was tested in the study. It can be noted that when assessing what model to choose, it depends on how the evaluation is done and which metric is deemed most important. Besides that wind speed will have the most significant impact in all models, forecasting error seem to have correlation to the diurnal cycle. One reason could be land-sea interaction during the day, especially at the period April-September. Higher forecasting errors correlates strongly to periods of a higher mean wind speed and times of varying weather will impact the forecastability and larger errors should be expected. In this project, numerical weather prediction data is used to investigate the forecasting error. A lower error can be seen at the first hours from the model run. This should be expected because it is when we are closest to the initial conditions, in other words, the real world. However, it seems like wind speed and diurnal cycle are more significant than the performance of the numerical weather prediction model in the first 24 hours.  Predicting the future power output of wind assets is expected to be even more impor- tant in the future years due to larger installed capacity. Even with an increase in installed capacity, an over capacity is not wanted and flexibility will be more important. There are challenges, but also opportunity to have a more efficient use of resources in our society and lowering the climate impact that our society has on the planet through a more flexible use of resources.