Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach
Abstract
This paper presents an integrated model for bidding energy storage in day-ahead and real-time markets to maximize profits. We show that in integrated two-stage bidding, the real-time bids are independent of day-ahead settlements, while the day-ahead bids should be based on predicted real-time prices. We utilize a transformer-based model for real-time price prediction, which captures complex dynamical patterns of real-time prices, and use the result for day-ahead bidding design. For real-time bidding, we utilize a long short-term memory-dynamic programming hybrid real-time bidding model. We train and test our model with historical data from New York State, and our results showed that the integrated system achieved promising results of almost a 20\% increase in profit compared to only bidding in real-time markets, and at the same time reducing the risk in terms of the number of days with negative profits.
Keywords
Cite
@article{arxiv.2404.17683,
title = {Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach},
author = {Saud Alghumayjan and Jiajun Han and Ningkun Zheng and Ming Yi and Bolun Xu},
journal= {arXiv preprint arXiv:2404.17683},
year = {2024}
}