Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach
This addresses the gap between prediction accuracy and decision-making efficiency in energy management, offering a domain-specific incremental improvement.
The paper tackles the problem of electricity price prediction for energy storage system arbitrage by proposing a decision-focused approach that integrates downstream optimization into training, resulting in increased economic benefits and reduced decision errors compared to traditional error-minimizing models.
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.