Optimization and Forecasting Models for Electricity Market and Renewable Energies

This thesis presents different optimization and forecasting models, with the focus on energy markets and renewable energy sources. The analysis approach is related to models for wind and solar power forecasts and those for electricity prices forecasts. The first study explores a Principal Component Analysis in combination with two post-processing techniques for the prediction of wind power and of solar irradiance produced over two large areas. The Principal Component Analysis is applied to reduce the datasets dimension. A Neural Network and an Analog Ensemble post-processing are then applied on the PCA output to obtain the final forecasts. The study shows that combining PCA with these post-processing techniques leads to better results when compared to the implementation without the PCA reduction. The second work explores two different techniques for the prediction of the Italian day-ahead electricity market prices. The predicted Italian prices are the zonal prices and the uniform purchase price (Prezzo Unico Nazionale or PUN). The study is conducted using hourly data of the prices to be predicted and a large set of variables used as predictors (i.e. historical prices, forecast load, wind and solar power forecasts, expected plenty or shortage of hydroelectric production, net transfer capacity available at the interconnections and the gas prices). A Neural Network and a Support Vector Regression are applied on the different predictors to obtain the final forecasts. Different predictors’ combinations are analysed to find the best forecast. The results show that the best configuration is obtained using all the predictors together and applying the Neural Network to find the forecasted prices.