Parametric and Generative Forecasts of Day-Ahead Market Curves for Storage Optimization

TL;DR

This paper introduces two machine learning frameworks for forecasting day-ahead market curves and optimizing storage, combining a fast parametric model for daily use with a generative model for detailed scenario analysis.

Contribution

It presents a novel combination of parametric and generative machine learning models for market curve forecasting and storage optimization in energy markets.

Findings

Low-error, interpretable demand and supply forecasts

Synthetic scenario generation of order-level submissions

Optimized storage strategies with revenue analysis

Abstract

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.