Nov. 16, 2022, 2:11 a.m. | Ningkun Zheng, Xiaoxiang Liu, Bolun Xu, Yuanyuan Shi

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv energy energy storage function prediction price storage value

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