Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910426257686528 |
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| author | Alghumayjan, Saud Han, Jiajun Zheng, Ningkun Yi, Ming Xu, Bolun |
| author_facet | Alghumayjan, Saud Han, Jiajun Zheng, Ningkun Yi, Ming Xu, Bolun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17683 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach Alghumayjan, Saud Han, Jiajun Zheng, Ningkun Yi, Ming Xu, Bolun Optimization and Control Computer Science and Game Theory Machine Learning Systems and Control 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. |
| title | Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach |
| topic | Optimization and Control Computer Science and Game Theory Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2404.17683 |