http://pierrepinson.com/31761/Literature/foley2012.pdf

Current methods and advances in forecasting of wind power generation

Energy storage for mitigating the variability of renewable electricity sources: An updated review

This paper presents an overview of existing research on wind speed and power forecasting. It first discusses state-of-the-art wind speed and power forecasting approaches. Then, forecasting accuracy is presented based on variable factors. Finally, potential techniques to improve the accuracy of forecasting models are reviewed. A full survey on all existing models is not presented, but attempts to highlight the most promising body of knowledge concerning wind speed and power forecasting.

Current status and future advances for wind speed and power forecasting

With the continuous increase of wind power penetration in power systems, the problems caused by the volatile nature of wind speed and its occurrence in the system operations such as scheduling and dispatching have drawn attention of system operators, utilities and researchers towards the state-of-the-art wind speed and power forecasting methods These methods have the required capability of reducing the influence of the intermittent wind power on system operations as well as of harvesting the wind energy effectively In this context, combining different methodologies in order to circumvent the challenging model selection and take advantage of the unique strength of plausible models have recently emerged as a promising research area Therefore, a comprehensive research about the combined models is called on for how these models are constructed and affect the forecasting performance Aiming to fill the mentioned research gap, this paper outlines the combined forecasting approaches and presents an up-to date annotated bibliography of the wind forecasting literature Furthermore, the paper also points out the possible further research directions of combined techniques so as to help the researchers in the field develop more effective wind speed and power forecasting methods

A review of combined approaches for prediction of short-term wind speed and power

The randomness and intermittence of wind resources is the biggest challenge in the integration of wind power into the power system. Accurate forecasting of wind power generation is an efficient tool to deal with such problem. Conventional wind power forecasting produces a value, or the conditional expectation of wind power output at a time point in the future. However, any prediction involves inherent uncertainty. In recent years, several probabilistic forecasting approaches have been reported in wind power forecasting studies. Compared to currently wide-used point forecasts, probabilistic forecasts could provide additional quantitative information on the uncertainty associated with wind power generation. For decision-makings in the uncertainty environment, probabilistic forecasts are optimal inputs. A review of state-of-the-art methods and new developments in wind power probabilistic forecasting is presented in this paper. Firstly, three different representations of wind power uncertainty are briefly introduced. Then, different forecasting methods are discussed. These methods are classified into three categories in terms of uncertainty representation, i.e. probabilistic forecasts (parametric and non-parametric), risk index forecasts and space-time scenario forecasts. Finally, requirements and the overall framework of the uncertainty forecasting evaluation are summarized. In addition, this article also describes current challenges and future developments associated with wind power probabilistic prediction.

Review on probabilistic forecasting of wind power generation

Wind power forecasting tools have been developed for some time. The majority of such tools usually provides single-valued (spot) predictions. Such predictions limits the use of tools for decision-making under uncertainty. In this paper we propose a method for producing the complete predictive probability density function (PDF). The method is based on kernel density estimation techniques. The preliminary results show that this method levels with state of the art one while being fast and producing the complete PDF. The results were obtained through real data from three French wind farms.

Probabilistic Short-term Wind Power Forecasting for the Optimal Management of Wind Generation

This presentation is based on a longer report trying to summarise more than 100 papers written in the field over the last decades. Many regions have nowadays such high penetrations of wind energy (the host region Western Denmark has in the order of 25%) that without good short-term prediction tools, an economic and secure integration of wind power with maximal ecological benefits of the wind power could not be realised. A historical perspective will lead to an account of the current crop of models, including to a high degree the experiences made in Denmark with operative use of the tools since 1994. Three horizons are interesting for utilities: a short horizon determined by the ramping and start-up times of conventional power plants for the scheduling (4-8 hours ahead), a longer horizon dealing with the trading on the different electricity exchanges (in the case of NordPool, 13-37 hours ahead), and a long horizon where the models could be used for maintenance planning (all the way to weeks ahead). For the first case, one could get away with using a time-series analysis model coupled to climatology, but for even medium horizons, the accuracy of the model is getting much better by using numerical weather prediction, typically from the local meteorological institute. The most prominent source of error is the numerical weather prediction model used, and in that it is phase errors (timing errors) that have a decisive impact on the traditional error scores, and on the financial bottom line. Current work is trying to estimate the uncertainty of the forecasts. The longer report has been prepared in the framework of work for the EU Commission, especially a Marie-Curie-Fellowship and the ANEMOS project.

The state of the art in short term prediction of wind power - from an offshore perspective

Short-term wind power forecasting is recognized nowadays as a major requirement for a secure and economic integration of wind power in a power system. In the case of large-scale integration, end users such as transmission system operators focus on the prediction of regional or even national wind power up to 48 hours ahead. At a European level such predictions will be required in the future for planning power exchanges between regions or countries. The main difficulty for predicting regional wind power is that on-line information is not available for all concerned wind farms. Predictions have to be based on a limited number of representative wind farms for which SCADA data and/or Numerical Weather Predictions are available and then extrapolated (“upscaled”) to predict the total wind power. In this work several approaches were developed for upscaling ranging from simple to more complex ones (i.e. based on artificial intelligence methods such as fuzzy-neural networks). Evaluation results are provided for the case of the Irish power system. Predictions for the output of eleven wind farms are made from a number of one up to five representative wind parks. The performance of the various approaches is evaluated using one year of data. Useful conclusions are derived for the impact of the “smoothing effect” on the performance of Persistence and that of advanced models.

https://hal-mines-paristech.archives-ouvertes.fr/hal-00530550/document

Forecasting of regional wind generation by a dynamic fuzzy-neural networks based upscaling approach

This paper describes some of the statistical methods considered in the ANEMOS project for short-termforecasting of wind power. The total procedure typically involves various steps, and all these steps are described in the paper. These steps include downscaling from reference MET forecasts to the actual wind farm, wind farm power curve models, dynamical models for prediction of wind power or wind speed, estimating the uncertainty of the wind power forecast, and finally, methods for upscaling are considered. The upscaling part considers how a total regional production can be estimated using a small number of reference wind farms. Keywords: Forecasting, power curve, wind farmpower curve, upscaling, uncertainty estimation, probabilistic forecasts, adaptation.

https://hal-mines-paristech.archives-ouvertes.fr/hal-00526680/document

Short-term Wind Power Forecasting Using Advanced Statistical Methods

https://hal-mines-paristech.archives-ouvertes.fr/hal-01626067/document

Storage sizing for grid connected hybrid wind and storage power plants taking into account forecast errors autocorrelation

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