Value-oriented price forecasting for arbitrage strategies of Energy Storage Systems through loss function tuning

Increasing shares of renewable generation are leading to more volatile electricity prices, presenting an opportunity for Energy Storage Systems (ESS) participating in short-term electricity markets. Model Predictive Control (MPC) has been shown to be a powerful tool to leverage the latest information at the time of optimization, yet its efficacy depends on the quality of the employed price forecasts. So far, these forecasts have been developed with traditional forecasting methods instead of value-oriented approaches, which consider the downstream decision problem during the forecaster training phase. Existing value-oriented methods, however, often rely on a specific downstream problem structure. This paper addresses these shortcomings by introducing a universally applicable, value-oriented forecasting methodology that employs a generalized loss function designed to account for inter-temporal price variability, using the downstream value (i.e., profit from ESS market participation) as the selection criterion in the hyperparameter tuning step. The proposed methodology is tested on a case study considering different types of ESS participating in the Belgian balancing market through MPC. The method is benchmarked against other forecasting techniques including a neural network trained in traditional, accuracy-oriented fashion. Using real-life data over a test set of two months, we show that the methodology outperforms those traditional techniques in terms of ex-post out-of-sample profit.