Forecast market prices before battery dispatch

To schedule a battery before delivery, an operator must estimate the periods with the lowest and highest prices before realised prices become available. Forecast error can shift charge or discharge away from the profitable spread, which reduces asset value and delays payback.

The case covers distinct market structures in Germany, Spain, and Denmark. This market range tests whether one forecasting method can support a fleet without a separate model for each location.

To compare each forecasting method, the benchmark uses the same prices, delivery days, and charge-discharge logic for every model. The comparison includes previous-day prices, classical machine-learning methods, another foundation model, and eomer models.

The commercial translation converts each forecast into an asset schedule. It therefore evaluates captured spread value rather than forecast error alone.

The day-ahead benchmark reports that eomer captures 87% to 92% of perfect-foresight arbitrage value across three European markets. For the illustrative German asset, the case estimates about EUR 4.5 million of additional annual value per GWh, or 32% above the baseline forecast method.

This result supports one reusable forecasting method across a battery fleet. Asset constraints, degradation cost, efficiency, and market access must still enter the dispatch model for each deployment.