  This paper proposes a novel energy storage price arbitrage algorithm
combining supervised learning with dynamic programming. The proposed approach
uses a neural network to directly predicts the opportunity cost at different
energy storage state-of-charge levels, and then input the predicted opportunity
cost into a model-based arbitrage control algorithm for optimal decisions. We
generate the historical optimal opportunity value function using price data and
a dynamic programming algorithm, then use it as the ground truth and historical
price as predictors to train the opportunity value function prediction model.
Our method achieves 65% to 90% profit compared to perfect foresight in case
studies using different energy storage models and price data from New York
State, which significantly outperforms existing model-based and learning-based
methods. While guaranteeing high profitability, the algorithm is also
light-weighted and can be trained and implemented with minimal computational
cost. Our results also show that the learned prediction model has excellent
transferability. The prediction model trained using price data from one region
also provides good arbitrage results when tested over other regions.
