Understanding the benefits of dynamic line rating under multiple sources of uncertainty

This paper analyses the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the re-dispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modelling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of DLR in supporting costeffective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyses the impact of different operational strategies, the coordination among multiple flexible technologies and installed capacity of wind generation on the benefits of DLR

Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering

International audienceThis paper studies the application of Kalman filtering as a post-processing method in numerical predictions of wind speed. Two limited-area atmospheric models have been employed, with different options/capabilities of horizontal resolution, to provide wind speed forecasts. The application of Kalman filter to these data leads to the elimination of any possible systematic errors, even in the lower resolution cases, contributing further to the significant reduction of the required CPU time. The potential of this method in wind power applications is also exploited. In particular, in the case of wind power prediction, the results obtained showed a remarkable improvement in the model forecasting skill

Evaluation of Advanced Wind Power Forecasting Models – Results of the Anemos Project

Disponible : http://www.ewec2006proceedings.info/allfiles2/969_Ewec2006fullpaper.pdfInternational audienceAn outstanding question posed today by end-users like power system operators, wind power producers or traders is what performance can be expected by state-of-the-art wind power prediction models. This paper presents results of the first ever intercomparison of a number of advanced prediction systems performed in the frame of the European project Anemos. A framework for error characterization has been developed consisting by a measure- and a distribution-oriented approach. This comparison has given a perspective of the possibilities and limitations of the forecasts in the different test cases that were defined. At a second stage, the homogenous comparison process has permitted to evaluate the possibility of obtaining better performance by exploiting the merits of individual models through model combination. The paper presents the methodology and results from the combination approach

Skill forecasting from ensemble predictions of wind power

International audienceOptimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set of alternative scenarios for the coming period) for a single prediction horizon or over a look-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power ensemble predictions are derived from the conversion of ECMWF and NCEP ensemble forecasts of meteorological variables to wind power ensemble forecasts, as well as by a lagged average approach alternative. The ability of prediction risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

Short-term Wind Power Forecasting Using Advanced Statistical Methods

Disponible sur : http://anemos.cma.fr/download/publications/pub_2006_paper_EWEC06_WP3statistical.pdfInternational audienceThis 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

Anemos : development of a next generation wind power forecasting system for the large-scale integration of onshore & offshore wind farms

International audienceThis paper presents the objectives and the research work carried out in the frame of the ANEMOS project on short-term wind power forecasting. The aim of the project is to develop accurate models that substantially outperform current state-of-the-art methods, for onshore and offshore wind power forecasting, exploiting both statistical and physical modeling approaches. The project focus on prediction horizons up to 48 hours ahead and investigates predictability of wind for higher horizons up to 7 days ahead useful i.e. for maintenance scheduling. Emphasis is given on the integration of highresolution meteorological forecasts. For the offshore case, marine meteorology is considered as well as information by satellite-radar images. An integrated software platform, ‘ANEMOS', is developed to host the various models. This system will be installed by several utilities for on-line operation at onshore and offshore wind farms for prediction at a local, regional and national scale. The applications include different terrain types and wind climates, on- and offshore cases, and interconnected or island grids. The on-line operation by the utilities will allow validation of the models and an analysis of the value of wind prediction for a competitive integration of wind energy in the developing liberalized electricity markets in the EU

Next Generation Short-Term Forecasting of Wind Power – Overview of the ANEMOS Project.

International audienceThe aim of the European Project ANEMOS is to develop accurate and robust models that substantially outperform current state-of-the-art methods, for onshore and offshore wind power forecasting. Advanced statistical, physical and combined modelling approaches were developed for this purpose. Priority was given to methods for on-line uncertainty and prediction risk assessment. An integrated software platform, 'ANEMOS', was developed to host the various models. This system is installed by several end-users for on-line operation and evaluation at a local, regional and national scale. Finally, the project demonstrates the value of wind forecasts for the power system management and market integration of wind power. Keywords: Wind power, short-term forecasting, numerical weather predictions, on-line software, tools for wind integration