ABSTRACTThis paper is concerned with evaluating techniques to forecast plausible future scenarios in wind power production for up to 48 h ahead, where the term scenario refers to a coherent chronological prediction including the timing, rapidity and size of large changes. Such predictions are of great interest in power systems with high regional wind penetration where a large rapid change in wind power may pose a threat to power system security. Numerous studies have evaluated wind power forecasting methods on ex post statistical measures of forecast accuracy such as root mean square error. Other work has assessed the forecast value by simulating automated decision making for bidding wind generation into particular electricity markets, and in some cases, the ex ante value of a perfect forecast has been assessed. The future, however, will always be uncertain, and decision making always takes place in an ex ante context. This paper discusses how numerical weather prediction (NWP) systems forecasts are produced, with a particular focus on uncertainty and how forecasters might visually present plausible future scenarios for wind power to electricity industry decision makers. It is difficult to quantify the ex ante value of visual wind power forecast information to the complex decision‐making process involved. Consequently, this paper explores qualitative assessments of ex ante value by proposing six desirable attributes for the techniques and the presentation of NWP forecasts to decision makers. It uses these attributes to assess four such methodologies, which include NWP ensemble methods and the recently introduced NWP spatial field approach. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.

This paper explores wind power integration issues for the South Australian (SA) region of the Australian National Electricity Market (NEM) by assessing the interaction of regional wind generation, electricity demand and spot prices over 2 recent years of market operation. SA's wind energy penetration has recently surpassed 20% and it has only a limited interconnection with other regions of the NEM. As such, it represents an interesting example of high wind penetration in a gross wholesale pool market electricity industry. Our findings suggest that while electricity demand continues to have the greatest influence on spot prices in SA, wind generation levels have become a significant secondary influence, and there is an inverse relationship between wind generation and price. No clear relationship between wind generation and demand has been identified although some periods of extremely high demand may coincide with lower wind generation. Periods of high wind output are associated with generally lower market prices, and also appear to contribute to extreme negative price events. The results highlight the importance of electricity market and renewable policy design in facilitating economically efficient high wind penetrations.

A critical limiting factor to the successful deployment of a large proportion of wind power in power systems is its predictability. Power system operators play a vital role in maintaining system security, and this task is greatly aided by useful characterizations of future system operations. A wind farm power forecast generally relies on the forecast output from a Numerical Weather Prediction (NWP) model, typically at a single grid point in the model to represent the wind farm's physical location. A key limitation of this approach is the spatial misplacement of weather features often found in NWP forecasts. This paper presents a methodology to display wind forecast information from multiple grid points at hub height around the wind farm location. If the raw forecast wind speeds at hub height at multiple grid points were to be displayed directly, they would be misleading as the NWP outputs take account of the estimated local surface roughness and terrain at each grid point. Hence, the methodology includes a transformation of the wind speed at each grid point to an equivalent value that represents the surface roughness and terrain at the chosen single grid point for the wind farm site. The chosen-grid-point-equivalent wind speeds for the wind farm can then be transformed to available wind farm power. The result is a visually-based decision support tool which can help the forecast user to assess the possibilities of large, rapid changes in available wind power from wind farms. A number of methods for displaying the field for multiple wind farms are discussed. The chosen-grid-point-equivalent transformation also has other potential applications in wind power forecasting such as assessing deterministic forecast uncertainty and improving downscaling results. Copyright © 2008 John Wiley & Sons, Ltd.

Growing concern about climate change is heightening the need for power systems to be able to operate cost-effectively with high levels of wind energy penetration. In a restructured electricity industry such as in Australia, commercial, regulatory and technical challenges must be solved in an integrated and compatible manner. The Australian National Electricity Market has a number of features that together create a unique situation for wind energy integration: ∞ It extends over 4000 km, with a single power system operator and market rules and with a largely consistent regulatory framework. ∞ It uses an energy-only market design, with a five-minute market for energy and frequencyrelated ancillary services with associated derivative markets. ∞ The system is of small rating, particularly given its geographical size, so that the transmission network is relatively weak and stability can be difficult to maintain. This paper describes the steps that have been taken to date in Australia to integrate wind energy in this context, as well as research that is currently under way to identify further design enhancements, considering engineering, economic and policy aspects.

Growing concern about climate change has led to significant interest in renewable energy resources such as wind energy. However, such non-storable energy sources present a significant issue - how to maintain continuity of supply in the event of possible disturbances to power production. For example, in the case of wind energy, such disturbances can result from extreme weather events due to frontal systems or rapidly evolving low pressure systems. Such events cannot be avoided, but if they can be accurately forecast, their impact can be minimized by ensuring that alternative sources are available to make up any power shortfalls. Thus as wind energy makes up an ever greater component of our energy supply, there is greater interest in developing models to produce accurate, local scale, wind-focused forecasts for wind farm sites that push the boundaries of current weather prediction techniques. In this article we present a case study focusing on the Woolnorth wind farm on the northwest tip of Tasmania, to highlight some of the key challenges that will be involved in developing such forecasts.

Successful integration of wind power into power systems can be facilitated through better understanding of future uncertainty in wind power generation. This paper explores a new approach to characterizing this uncertainty using measures of the variability in the wind speeds predicted at multiple grid points in a Numerical Weather Prediction (NWP) system. This approach is compared to the traditional approach of using the spread from an NWP ensemble by using two measures of uncertainty; forecast errors in single time-series forecasts and observed temporal variability. Results show that the multiple grid point approach has a comparable skill level to NWP ensembles for predicting these uncertainty measures and in particular, demonstrates very good skill in predicting large forecast errors. These results also provide a positive evaluation of a terrain standardization method described in a companion paper. A possible extension of this work is to combine the multiple grid point approach with NWP ensembles to improve uncertainty characterization.

This paper presents preliminary results from a wind power forecasting study of potentially high-risk scenarios for power systems in the context of the Australian National Electricity Market (NEM). This paper focuses on the two Australian States of highest wind penetration to date, South Australia and Tasmania. Firstly, a study is made of the wind climate of these two States affecting the wind power output at various wind farms. Techniques to identify specific instances of high-risk scenarios are explored combining aggregated wind power and meteorological observations with known inherent power system constraints. An assessment is made of the performance of two technically different, commercially available forecasting systems. Further work is planned to expand on these findings and develop ramp forecasting techniques as a complement to traditional approaches.

The Wind Power Prediction Tool (WPPT) has been installed in Australia for the first time, to forecast the power output from the 65MW Roaring 40s Renewable Energy P/L Woolnorth Bluff Point wind form. This article analyses the general performance of WPPT as well as its performance during large romps (swings) in power output. In addition to this, detected large ramps are studied in detail and categorized. WPPT combines wind speed and direction forecasts from the Australian Bureau of Meteorology regional numerical weather prediction model, MesoLAPS, with real-time wind power observations to make hourly forecasts of the wind farm power output. The general performances of MesoLAPS and WPPTore evaluated over I year using the root mean square error (RMSE). The errors are significantly lower than for basic benchmark forecasts but higher than for many other WPPT installations, where the site conditions are not as complicated as Woolnorth Bluff Point. Large ramps are considered critical events for a wind power forecast for energy trading as well as managing power system security. A methodology is developed to detect large ramp events in the wind farm power data. Forty-one large ramp events are detected over I year and these are categorized according to their predictability by MesoLAPS, the mechanical behaviour of the wind turbine, the power change observed on the grid and the source weather event. During these events, MesoLAPS and WPPT are found to give an RMSE only roughly equivalent to just predicting the mean (climatology forecast). Copyright (c) 2007 John Wiley & Sons, Ltd.

A new optimised clustering method is presented for generating wind classes for mesoscale modelling to produce numerical wind atlases. It is compared with the existing method of dividing the data in 12 to 16 sectors, 3 to 7 wind-speed bins and dividing again according to the stability of the atmosphere.Wind atlases are typically produced using many years of on-site wind observations at many locations. Numerical wind atlases are the result of mesoscale model integrations based on synoptic scale wind climates and can be produced in a number of hours of computation. 40 years of twice daily NCEP/NCAR Reanalysis geostrophic wind data (approximately 200 km resolution) are represented in typically around 150 classes, each with a frequency of occurrence. The mean wind-speed and direction in each class is used as input data to force the mesoscale model, which downscales the wind to a 5 km resolution while adapting to the local topography. The purpose of forming classes is to minimise the computational time for the mesoscale model while still representing the synoptic climate features.Only tried briefly in the past, clustering has traits that can be used to improve the existing class generation method by optimising the representation of the data and by automating the procedure more. The Karlsruhe Atmospheric Mesoscale Model (KAMM) is combined with the WAsP analysis to produce numerical wind atlases for two sites, Ireland and Egypt. The model results are compared with wind atlases made from measurements at specific sites. The sources are The New Irish Wind Resource Atlas and the Wind Atlas for the Gulf of Suez. The new clustering method has the ability to include wind-speed, direction and thermal stability from different heights for the classification. It is shown that the clustering method is able to produce results at least as accurate as the existing method for both sites. A refined, general clustering procedure is devised which could improve the results for both sites, where the existing method requires two different configurations.

The leading Danish statistical wind power forecasting system, the Wind Power Prediction Tool (Zephyr - WPPT) has been installed in Australia for the first time at Hydro Tasmania. The system was commissioned in March 2006. Currently WPPT is forecasting the power output from the 64.75 MW Woolnorth Bluff Point wind farm and the forecasts will soon be used by Hydro Tasmania's electricity traders in the NEMMCO spot market. WPPT is running operationally and predicting the wind power output in hourly steps for a prediction horizon of up to 42 hours. It combines historical wind farm power output data back to October 2004 with current wind speed and direction forecasts for the region obtained twice a day from the Australian Bureau of Meteorology. The results for one full year of WPPT operation are presented. This includes an assessment of the general performance of WPPT in predicting the wind farm output, as well as its performance during large-swings in wind power output. The potential benefits of the forecasts to the electricity market management and the scheduling of the hydro power plants are discussed.