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

TL;DR

This paper introduces a transformer-based approach for energy storage arbitrage in two-settlement markets, combining price prediction and bidding strategies to enhance profits and reduce risks.

Contribution

It presents a novel integrated model utilizing transformer-based price prediction and LSTM-DP bidding for energy storage arbitrage in two-settlement markets.

Findings

Achieved nearly 20% profit increase over real-time-only bidding.

Reduced the number of days with negative profits.

Validated with historical data from New York State.

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.