Portfolio balancing strategy for the integration of renewable energy sources to the day ahead market

Sammanfattning: New methods of government support and marketing of renewable production push the renewable energy sources (RES) to be more integrated to the wholesale day-ahead market. In this way, predictive models of production for solar and wind power have been developed to manage the resulting balancing costs. They aim to forecast the production of a plant for the following day at hourly intervals, based on historical operational and meteorological data. They are backed by three machine learning algorithms, which are the Artificial Neural Networks (ANN), the Support Vector Regression (SVR) and the Random Forest (RF).  These models are evaluated on 20 solar farms and 2 wind farms, through 3 criteria which are the RMSE, the RMSEN and the R-square. It gives significantly improved performances compared to ‘persistence method’ or other naive methods. In most cases, the best results were obtained with the random forest algorithm, with an average RMSEN of 15% and an average R-square of 0,8. Considering these models and ideal operational conditions, the balancing costs are evaluated for each solar farm, showing the lowest obtainable costs with these models. The average cost calculated ranges from 1 to 1,4 € per MWh produced depending on the power plant considered. However, thanks to the ‘portfolio benefit effect’, the combination of the forecasting errors of multiple sites can highly decrease this cost. Strategies of portfolio combination can be developed by increasing the installed capacity and the number of sites within the portfolio and/or diversifying the locations or the types of RES used. The savings go up to 45% of the initial simple balancing costs.