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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

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Publisher copy:
10.1016/j.cie.2026.112112

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-3211-2579
More by this author
Role:
Author
ORCID:
0000-0003-0030-9238
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Hertford College
Role:
Author
ORCID:
0000-0003-2781-9588


More from this funder
Funder identifier:
https://ror.org/019w4f821
Grant:
101148367
Programme:
Horizon Europe programme
More from this funder
Funder identifier:
https://ror.org/01nrxwf90


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:

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