BUSCADOR RECOLECTA

For the correct operation of the electricity system, producers must provide an estimate of the energy they are going to discharge into the system, and they must face financial penalties if their forecasts are wrong. This is especially difficult in the case of renewable energies, and in particular wind energy because of its variability and intermittency. The tool proposed allows, in a first step, to improve the prediction of wind energy to be produced and, in a second step, to optimize the offer to be presented to the electricity market, so that the overall economic performance can be improved. This tool is based on the use of public information and automatic learning and has been evaluated on a set of 30 wind farms in Spain, using their historical production data. The results indicate improvements in both the accuracy of the energy estimation and the profit obtained from the energy sold., This work has been supported by the Ministerio de Ciencia, Innovación y Universidades, Spain, grant contract RTC-2017-6635-3; by the Ministerio de Economía y Competitividad, Spain, grant contract PID2019-107685RB-I00; by the Fundación General de la Universidad de Salamanca, Spain, grant contract PC_TCUE2-23_012; by the European Regional Development Fund (ERDF) and the Department of Education of the regional government, the Junta of Castilla y León, Spain, grant contract SA089P20; and by the European Union's Horizon 2020 - Research and Innovation Framework Program under grant agreement ID 101036926.

Wind speed forecasts obtained by Numerical Weather Prediction models are limited for fine interpretation in
heterogeneous terrain, in which different roughnesses and orographies occur. This limitation is derived from the
use of low-resolution and grid-box averaged data. In this paper a dynamical downscaling method is presented to
increase the local accuracy of wind speed forecasts. The proposed method divides the wind speed forecasting
into two steps. In the first one, the mesoscale model WRF (Weather Research and Forecasting) is used for getting
wind speed forecasts at specific points of the study domain. On a second stage, these values are used for feeding
the HDWind microscale model. HDWind is a local model that provides both a high-resolution wind field that
covers the entire study domain and values of wind speed and direction at very located points. As an example of
use of the proposed method, we calculate a high-resolution wind field in an urban-interface area from Badajoz, a
South-West Spanish city located near the Portugal border. The results obtained are compared with the values
read by a weathervane tower of the Spanish State Meteorological Agency (AEMET) in order to prove that the
microscale model improves the forecasts obtained by the mesoscale model., This work has been supported by the Ministerio de Ciencia, Innovación y Universidades, Grant contract: RTC-2017-6635-3, and by the University of Salamanca General Foundation, Grant contract: PC_TCUE15-17_F2_02

Wind energy forecasting is a critical aspect for wind energy producers, given that the chaotic nature and the intermittence of meteorological wind cause difficulties for both the integration and the commercialization of wind-produced electricity. For most European producers, the quality of the forecast also affects their financial outcomes since it is necessary to include the impact of imbalance penalties due to the regularization in balancing markets. To help wind farm owners in the elaboration of offers for electricity markets, the EOLO predictor model can be used. This tool combines different sources of data, such as meteorological forecasts, electric market information, and historic production of the wind farm, to generate an estimation of the energy to be produced, which maximizes its financial performance by minimizing the imbalance penalties. This research study aimed to evaluate the performance of the EOLO predictor model when it is applied to the different Spanish electricity markets, focusing on the statistical analysis of its results. Results show how the wind energy forecast generated by EOLO anticipates real electricity generation with high accuracy and stability, providing a reduced forecast error when it is used to participate in successive sessions of the Spanish electricity market. The obtained error, in terms of RMAE, ranges from 8%, when it is applied to the Day-ahead market, to 6%, when it is applied to the last intraday market. In financial terms, the prediction achieves a financial performance near 99% once imbalance penalties have been discounted., This work has been supported by the the Ministerio de Ciencia, Innovación y Universidades, grant contract: RTC-2017-6635-3; by the University of Salamanca General Foundation, grant contract: PC_TCUE2-23_012; by the European Regional Development Fund (ERDF) and the Department of Education of the regional government, the Junta of Castilla y León, grant contract SA089P20; and by the European Union’s Horizon 2020—Research and Innovation Framework Program under grant agreement ID 101036926.