Forecasting Electricity Prices with Decision-Focused Learning for Storage Optimization

Abstract

Energy Storage Systems (ESS) play a crucial role in managing renewable energy variability. Forecast-informed optimization is typically used to maximize ESS profit in electricity markets. Whereas traditional forecaster training methods use accuracy-based loss functions, Decision-Focused Learning uses a task-aware loss function with the aim of improving ESS profits. This can be achieved by integrating the downstream optimization in the forecaster training procedure. A common task-aware loss function is the Smart Predict-then-Optimize (SPO+) loss. However, its current implementation is prone to overfitting and is limited to linear forecasting models. Here, we extend the SPO+ framework to neural network forecasters with non-linear activation functions while introducing an interior-point training method to mitigate overfitting risks. When applied to an ESS participating in the day-ahead market, our approach outperforms both traditional and other decision-focused benchmarks in terms of obtained ESS profits.