Parametric and Generative Forecasts of Day-Ahead Market Curves for Storage Optimization
This work addresses forecasting and optimization for energy storage in day-ahead markets, which is incremental as it applies existing methods to a specific domain problem.
The paper tackled forecasting aggregated supply and demand curves in the EPEX SPOT day-ahead market using parametric and generative machine learning models, resulting in optimized storage strategies that quantified revenue distributions and highlighted price-compression effects with lower peaks and higher off-peak levels.
We present two machine learning frameworks for forecasting aggregated curves and optimizing storage in the EPEX SPOT day-ahead market. First, a fast parametric model forecasts hourly demand and supply curves in a low-dimensional and grid-robust representation, with minimum and maximum volumes combined with a Chebyshev polynomial for the elastic segment. The model enables daily use with low error and clear interpretability. Second, for a more comprehensive analysis, though less suited to daily operation, we employ generative models that learn the joint distribution of 24-hour order-level submissions given weather and fuel variables. These models generate synthetic daily scenarios of individual buy and sell orders, which, once aggregated, yield hourly supply and demand curves. Based on these forecasts, we optimize a price-making storage strategy, quantify revenue distributions, and highlight the price-compression effect with lower peaks, higher off-peak levels, and diminishing returns as capacity expands.