# Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach

arXiv: https://arxiv.org/abs/2404.17683 · HTML: https://mlsanity.com/paper/2404.17683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark](https://mlsanity.com/paper/2008.08004/) (2020)
- [Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning](https://mlsanity.com/paper/1711.03127/) (2017)
- [Algorithmic Bidding for Virtual Trading in Electricity Markets](https://mlsanity.com/paper/1802.03010/) (2018)
- [Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis](https://mlsanity.com/paper/2104.02754/) (2021)
- [Optimal Battery Participation in Frequency Regulation Markets](https://mlsanity.com/paper/1710.10514/) (2017)
- [Arbitraging Variable Efficiency Energy Storage using Analytical Stochastic Dynamic Programming](https://mlsanity.com/paper/2108.06000/) (2021)
- [Arbitrage with Power Factor Correction using Energy Storage](https://mlsanity.com/paper/1903.06132/) (2019)
- [Transferable Energy Storage Bidder](https://mlsanity.com/paper/2301.01233/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets](https://mlsanity.com/paper/2602.16735/) (2026)

## Abstract (arXiv)

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.