Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices

Highlights: • Transmission System Operators (TSO) forecasts of fundamental variables (load, wind and solar) are biased • The TSO predictions of load can be significantly improved by applying ARX types of models • Enhanced predictions of generation structure help to improve the accuracy of day-ahead and intraday price forecasts • Improved forecasts of electricity prices can be successfully used in the decision process and bring additional revenue In recent years, a rapid development of renewable energy sources (RES) has been observed across the world. Intermittent energy sources, which depend strongly on weather conditions, induce additional uncertainty to the system and impact the level and variability of electricity prices. Predictions of RES, together with the level of demand, have been recognized as one of the most important determinants of future electricity prices. In this research, it is shown that forecasts of these fundamental variables, which are published by Transmission System Operators (TSO), are biased and could be improved with simple regression models. Enhanced predictions are next used for forecasting of spot and intraday prices in Germany. The results indicate that improving the forecasts of fundamentals leads to more accurate predictions of both, the spot and the intraday prices. Finally, it is demonstrated that utilization of enhanced forecasts is helpful in a day-ahead choice of a market (spot or intraday), and results in a substantial increase of revenues.