Deep learning-based forecasting of the automatic Frequency Reserve Restoration band price in the Iberian electricity market

Abstract

The replacement of conventional and dispatchable generation technologies by intermittent renewable energy sources increases the need for ancillary services. New agents, such as batteries, may join frequency regulation markets but they require accurate information about future market prices and service demand trends in order to make their participation profitable. This paper proposes and analyzes the accuracy of various deep learning-based models to estimate the secondary reserve marginal band price in the automatic frequency restoration reserves service of the Iberian electricity market. First, a correlation analysis allows determining various subsets of market variables used as model inputs. These subsets include some highly correlated variables together with different combinations of others whose influenced is analyzed. Next, three different neural network techniques are considered: feedforward, convolutional and recurrent networks. For each of them, a random search is performed to obtain the best set of hyperparameters. The analysis of the results shows how the LSTM model returns the best performance metrics (63.22 % of mean absolute scaled error), clearly improving the state-of-the-art in the domain.