Wind power forecasting

A wind power forecast corresponds to an estimate of the expected production of one or more wind turbines (referred to as a wind farm) in the near future, up to a year. Forecast are usually expressed in terms of the available power of the wind farm, occasionally in units of energy, indicating the power production potential over a time interval. Forecasting of the wind power generation may be considered at different time scales, depending on the intended application: very short-term forecasts (from seconds up to minutes) are used for the real-time turbine control and electrical grid management, as well as for market clearing; short-term forecasts (from 30 minutes up to hours) are used for dispatch planning, intelligent load shedding decisions; medium-term forecasts (from 6 hours up to a day) are used for to make decisions for switching the turbine on or off for safety or conditions on the market; long-term forecasts (from a day up to a week or even a year) are used for long term planning (to schedule the maintenance or unit commitment, optimize the cost of operation). Maintenance of offshore wind farms may be particularly costly, so optimal planning of maintenance operations is of particular importance. For the last two possibilities, the temporal resolution of wind power predictions ranges between 10 minutes and a few hours (depending on the forecast length). Improvements of wind power forecasting has focused on using more data as input to the models involved, and on providing uncertainty estimates along with the traditionally provided predictions. In the electricity grid at any moment balance must be maintained between electricity consumption and generation – otherwise disturbances in power quality or supply may occur. Wind generation is a direct function of wind speed and, in contrast to conventional generation systems, is not easily dispatchable, so fluctuations of wind generation require power substitution from other sources that might not be available on a short notice (it takes 6 hours to fire up a coal plant and 12 hours for a nuclear one).

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Fernando Porté Agel, Jiannong Fang, Lorenzo Donadio

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

2021

Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics

Fernando Porté Agel, Jiannong Fang, Daniel Jacob Tabas

With rising levels of wind power penetration in global electricity production, the relevance of wind power prediction is growing. More accurate forecasts reduce the required total amount of energy reserve capacity needed to ensure grid reliability and the risk of penalty for wind farm operators. This study analyzes the Computational Fluid Dynamics (CFD) software WindSim regarding its ability to perform accurate wind power predictions in complex terrain. Simulations of the wind field and wind farm power output in the Swiss Jura Mountains at the location of the Juvent Wind Farm during winter were performed. The study site features the combined presence of three complexities: topography, heterogeneous vegetation including forest, and interactions between wind turbine wakes. Hence, it allows a comprehensive evaluation of the software. Various turbulence models, forest models, and wake models, as well as the effects of domain size and grid resolution were evaluated against wind and power observations from nine Vestas V90’s 2.0-MW turbines. The results show that, with a proper combination of modeling options, WindSim is able to predict the performance of the wind farm with sufficient accuracy.

2019

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Fernando Porté Agel, Jiannong Fang, Lorenzo Donadio

2021

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics

Development and tuning of a coupled mesoscale/microscale model for wind forecasting at the Juvent wind farm

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics

Development and tuning of a coupled mesoscale/microscale model for wind forecasting at the Juvent wind farm