DER Day-Ahead Offering: A Neural Network Column-and-Constraint Generation Approach
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| Format: | Preprint |
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2025
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| _version_ | 1866912865519140864 |
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| author | Meng, Weiqi Li, Hongyi Cui, Bai |
| author_facet | Meng, Weiqi Li, Hongyi Cui, Bai |
| contents | In the day-ahead energy market, the offering strategy of distributed energy resource (DER) aggregators must be submitted before the uncertainty realization in the form of price-quantity pairs. This work addresses the day-ahead offering problem through a two-stage adaptive robust stochastic optimization model, wherein the first-stage price-quantity pairs and second-stage operational commitment decisions are made before and after DER uncertainty is realized, respectively. Uncertainty in day-ahead price is addressed using a stochastic programming-based approach, while uncertainty of DER generation is handled through robust optimization. To address the max-min structure of the second-stage problem, a neural network-accelerated column-and-constraint generation method is developed. A dedicated neural network is trained to approximate the value function, while optimality is maintained by the design of the network architecture. Numerical studies indicate that the proposed method yields high-quality solutions and is up to 100 times faster than Gurobi and 33 times faster than classical column-and-constraint generation on the same 1028-node synthetic distribution network. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_12384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DER Day-Ahead Offering: A Neural Network Column-and-Constraint Generation Approach Meng, Weiqi Li, Hongyi Cui, Bai Systems and Control Optimization and Control In the day-ahead energy market, the offering strategy of distributed energy resource (DER) aggregators must be submitted before the uncertainty realization in the form of price-quantity pairs. This work addresses the day-ahead offering problem through a two-stage adaptive robust stochastic optimization model, wherein the first-stage price-quantity pairs and second-stage operational commitment decisions are made before and after DER uncertainty is realized, respectively. Uncertainty in day-ahead price is addressed using a stochastic programming-based approach, while uncertainty of DER generation is handled through robust optimization. To address the max-min structure of the second-stage problem, a neural network-accelerated column-and-constraint generation method is developed. A dedicated neural network is trained to approximate the value function, while optimality is maintained by the design of the network architecture. Numerical studies indicate that the proposed method yields high-quality solutions and is up to 100 times faster than Gurobi and 33 times faster than classical column-and-constraint generation on the same 1028-node synthetic distribution network. |
| title | DER Day-Ahead Offering: A Neural Network Column-and-Constraint Generation Approach |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2511.12384 |