The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation.

International audienceA benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (" WIRE ") with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future

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

The new IEA Wind Task 36 on Wind Power Forecasting

International audienceWind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, …), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions

Prediction of waves, wakes and offshore wind - the results of the POW'WOW project

International audienceThe POWWOW project (Prediction of Waves, Wakes and Offshore Wind, a EU Coordination Action) aimed to develop synergy in the fields of wind and wave predictions from short to resource timescales by integrating modelling approaches currently used by the communities separately. The project aimed to help these research communities by establishing virtual laboratories, offering specialised workshops, and setting up expert groups with large outreach in the mentioned fields. In this paper, the main results of POWWOW are summarised

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

Smart4RES next-generation forecasting solutions for single wind turbines up to aggregations and for different temporal scales

International audienceThe Horizon2020 Smart4RES project develops forecasting and optimization solutions for the operation of Renewable Energy Sources (RES) and their application in electricity market trading and grid management. As a follow-up of the Poster presented in 2021, this poster presents the latest solutions developed in Smart4RES that intend to cover a large spectrum of prediction horizons and spatial scales in order to maximize the interest for the wind power industry.At the scale of a wind farm, the fluctuations of wind conditions at very-short-term horizons (seconds to minutes ahead) impose significant variations in the structural load of the turbine and its power output. These variations are challenging for a precise control of wind turbines. The forecasting models developed by Smart4RES make use of new data sources unexploited by traditional models such as 2-beam and 4-beam nacelle-mounted LIDAR. Results obtained on an operating turbine equipped with LIDAR show significant improvement in the forecasting error of both structural load and active power output for the next 20 seconds ahead. At the horizon of the next minutes, the power output of a wind farm is impacted by weather variability but also by wake losses that may vary as a function of turbine curtailment following a system operator request. This is why Smart4RES proposes a dynamic Machine Learning (ML) prediction model based on Transfer Learning which provides adaptive forecast that beat state-of-art approaches including a similar ML model trained only in batch mode. Aggregations of renewable power plants are key players for renewable-based provision of services to the grid and optimized management of distribution grids. In this context, a coherent forecast over the entire hierarchy of the aggregation is essential in order to take decisions that are feasible considering local constraints in the various levels of the hierarchy. A main challenge in such hierarchies is to produce a forecast even if data is missing, which can occur frequently at different periods and levels in the hierarchy. Whereas existing approaches tend to discard periods with missing data, which can drastically reduce the amount of available data for training and the applicability of the forecasting model in real conditions, Smart4RES proposes an end-to-end learning approach that is able to derive coherent and precise hierarchical forecasts even in the presence of missing values at different levels of the hierarchy

Overhead lines Dynamic Line rating based on probabilistic day-ahead forecasting and risk assessment

International audienceDynamic Line Rating is a technology devised to modify an overhead line's current-carrying capacity based on weather observation. The benefits of this modification may include reduced congestion costs, an increased renewable energy penetration rate, and improved network reliability. DLR is already well developed, but few papers in the literature investigate DLR day-ahead forecasting. The latter is central to DLR development since many of the decisions related to grid management are taken at least on a day-ahead basis. In this paper, two problems related to DLR forecasts are dealt with: how to achieve precise, reliable calculations of day-ahead forecasts of overhead line ampacity and how to define a methodology to calculate safe rating values using these forecasts. On the first point, four machine-learning algorithms were evaluated, identifying the best approach for this problem and quantifying the potential performance. On the second point, the developed methodology was tested and compared to the current static line rating approach

Reliable Provision of Ancillary Services from Aggregated Variable Renewable Energy Sources through Forecasting of Extreme Quantiles

International audienceVirtual power plants aggregating multiple renewable energy sources such as Photovoltaics and Wind are promising candidates for the provision of balancing ancillary services. A requisite for the provision of these services is that forecasts of aggregated production need to be highly reliable in order to minimize the risk of not providing the service. Yet, a reliability greater than 99% is unattainable for standard forecasting models. This work proposes alternative models for the day-ahead prediction of the lowest quantiles (0.1% to 0.9 %) of renewable Virtual power plant production. The proposed approaches derive conditional quantile forecasts of aggregated Wind/PV/Hydro production, obtained from tailored parametric models and machine learning models, including a Convolutional Neural Network architecture for predicting extremes. Reliability deviation is reduced up to 50 % and probabilistic skill score up to 18% compared to Quantile Regression Forest. Forecasting models are subsequently applied to the provision of downward reserve capacity by a renewable Virtual power plant. Increased forecasting reliability leads to a higher reliability of the reserve capacity, but reduces the average reserve volume offered by the renewable aggregation