Journal article
Scalable multi-level optimization for sequentially cleared energy markets with a case study on gas and carbon aware unit commitment
- Abstract:
- This paper examines Mixed-Integer Multi-Level problems with Sequential Followers (MIMLSF), a specialized optimization model aimed at enhancing upper-level decision-making by incorporating anticipated outcomes from lower-level sequential market-clearing processes. We introduce a novel approach that combines lexicographic optimization with a weighted-sum method to asymptotically approximate the MIMLSF as a single-level problem, capable of managing multi-level problems exceeding three levels. To enhance computational efficiency and scalability, we propose a dedicated Benders decomposition method with multi-level subproblem separability. To demonstrate the practical application of our MIMLSF solution technique, we tackle a unit commitment problem (UC) within an integrated electricity, gas, and carbon market clearing framework in the Northeastern United States, enabling the incorporation of anticipated costs and revenues from gas and carbon markets into UC decisions. This ensures that only profitable gas-fired power plants (GFPPs) are committed, allowing system operators to make informed decisions that prevent GFPP economic losses and reduce total operational costs under stressed electricity and gas systems. The case study not only demonstrates the applicability of the MIMLSF model but also highlights the computational benefits of the dedicated Benders decomposition technique, achieving average reductions of 32.23% in computing time and 94.23% in optimality gaps compared to state-of-the-art methods.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 3.6MB, Terms of use)
-
(Preview, Supplementary materials, pdf, 252.3KB, Terms of use)
-
- Publisher copy:
- 10.1016/j.cie.2026.112112
Authors
+ European Union
More from this funder
- Funder identifier:
- https://ror.org/019w4f821
- Grant:
- 101148367
- Programme:
- Horizon Europe programme
- Publisher:
- Elsevier
- Journal:
- Computers & Industrial Engineering More from this journal
- Volume:
- 218
- Article number:
- 112112
- Publication date:
- 2026-05-16
- Acceptance date:
- 2026-05-09
- DOI:
- EISSN:
-
1879-0550
- ISSN:
-
0360-8352
- Language:
-
English
- Keywords:
- Pubs id:
-
2421522
- Local pid:
-
pubs:2421522
- Source identifiers:
-
W4407764904
- Deposit date:
-
2026-05-19
- ARK identifier:
Terms of use
- Copyright holder:
- Xia et al.
- Copyright date:
- 2026
- Rights statement:
- ©2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record