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

2026-01-28

AI Summary

This study addresses the challenges of supply–demand curve forecasting and energy storage scheduling in day-ahead electricity markets by proposing a dual-framework approach that integrates parametric modeling with generative learning. On one hand, it employs Chebyshev polynomials to construct a low-dimensional, interpretable, and grid-constraint-compliant model for hourly supply–demand curve prediction. On the other, it leverages weather and fuel-related variables within a conditional generative model to synthesize 24-hour, order-level market scenarios for optimizing storage charge–discharge strategies. This work is the first to incorporate order-level scenario generation into storage optimization, enhancing scenario diversity without compromising real-time computational efficiency. Experimental results demonstrate that the proposed method achieves high-accuracy curve forecasting, significantly improves storage revenue, and reveals the price compression and diminishing marginal returns associated with capacity expansion.

Technology Category

• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• day-ahead market
• storage optimization
• curve forecasting
• price-making storage
• electricity market

Innovation

• parametric forecasting
• generative modeling
• day-ahead market curves
• storage optimization
• Chebyshev polynomial representation