A Value-Oriented Price Forecasting Approach to Optimize Trading of Renewable Generation

The participation of renewable generators in electricity markets involves employing a number of forecasting and decision-making tools. The standard approach consists in forecasting power output and market quantities, and then inputting the results into an optimization problem to derive optimal decisions. Typically, forecasting models are trained to optimize accuracy without considering the subsequent decision-making process. In this paper, we consider training forecasting models with a value-oriented approach that aims to minimize the suboptimality of decisions induced by a set of predicted inputs. We consider a risk-aware renewable generator participating in a day-ahead market subject to imbalance costs, and train ensembles of decision trees to forecast the imbalance penalty by directly minimizing trading costs for the provided strategy. The results indicate that our innovative approach leads to improved trading performance, compared to the standard method in which forecasting models are trained to minimize prediction errors.