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Electricity price prediction plays a vital role in energy storage system(ESS) management. Current prediction models focus on reducing prediction errorsbut overlook their impact on downstream decision-making. So this paper proposesa decision-focused electricity price prediction approach for ESS arbitrage tobridge the gap from the downstream optimization model to the prediction model.The decision-focused approach aims at utilizing the downstream arbitrage modelfor training prediction models. It measures the difference between actualdecisions under the predicted price and oracle decisions under the true price,i.e., decision error, by regret, transforms it into the tractable surrogateregret, and then derives the gradients to predicted price for trainingprediction models. Based on the prediction and decision errors, this paperproposes the hybrid loss and corresponding stochastic gradient descent learningmethod to learn prediction models for prediction and decision accuracy. Thecase study verifies that the proposed approach can efficiently bring moreeconomic benefits and reduce decision errors by flattening the timedistribution of prediction errors, compared to prediction models for onlyminimizing prediction errors.

Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach