  Electricity price prediction plays a vital role in energy storage system
(ESS) management. Current prediction models focus on reducing prediction errors
but overlook their impact on downstream decision-making. So this paper proposes
a decision-focused electricity price prediction approach for ESS arbitrage to
bridge the gap from the downstream optimization model to the prediction model.
The decision-focused approach aims at utilizing the downstream arbitrage model
for training prediction models. It measures the difference between actual
decisions under the predicted price and oracle decisions under the true price,
i.e., decision error, by regret, transforms it into the tractable surrogate
regret, and then derives the gradients to predicted price for training
prediction models. Based on the prediction and decision errors, this paper
proposes the hybrid loss and corresponding stochastic gradient descent learning
method to learn prediction models for prediction and decision accuracy. The
case study verifies that the proposed approach can efficiently bring more
economic benefits and reduce decision errors by flattening the time
distribution of prediction errors, compared to prediction models for only
minimizing prediction errors.
