Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach
This addresses profit optimization for energy storage operators in electricity markets, but it is incremental as it builds on existing bidding and prediction methods.
The paper tackled the problem of maximizing profits for energy storage in two-settlement markets by developing an integrated bidding model that uses a transformer for real-time price prediction to inform day-ahead bids and a hybrid model for real-time bidding. The result was a nearly 20% increase in profit compared to real-time-only bidding and reduced risk of negative profits.
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.