Generalizing Unit Commitment Problem Solving via SAT-based Decoupling

Fangji Fu; Han Huang; Yuxin Zhao; Zhifeng Hao

arxiv: 2604.16489 · v1 · submitted 2026-04-13 · 💻 cs.LO · cs.CE

Generalizing Unit Commitment Problem Solving via SAT-based Decoupling

Yuxin Zhao , Han Huang , Fangji Fu , Zhifeng Hao This is my paper

Pith reviewed 2026-05-10 15:30 UTC · model grok-4.3

classification 💻 cs.LO cs.CE

keywords unit commitmentSAT reductionproblem decouplingpower system optimizationsatisfiability solvinggeneralizabilityenergy transition

0 comments

The pith

Reducing all unit commitment variants to SAT instances lets one standard solver handle them all.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper proposes converting different versions of the unit commitment problem into satisfiability instances that off-the-shelf SAT solvers can process directly. The conversion decouples the solving procedure from the details of any particular variant, so the same algorithm applies without redesign. A reader would care because power systems face frequent new constraints from changing energy sources and regulations, rendering specialized solvers obsolete. If the reductions preserve problem structure, the method supplies a reusable way to address emerging formulations in evolving grids.

Core claim

The paper claims that uniformly reducing all unit commitment problem variants to SAT instances solvable by standard SAT solvers makes the solving algorithm independent of the original variant. This grants broad applicability across diverse variants. Experiments indicate that the method achieves better solution quality than specialized algorithms designed for particular variants and shows stronger generalizability to new problems.

What carries the argument

The SAT-based reduction that transforms the constraints and objectives of any unit commitment variant into an equivalent satisfiability instance.

If this is right

The same SAT solver applies to any new unit commitment formulation once its reduction is defined.
Solution quality exceeds that of algorithms built for a single variant.
New operational requirements in power systems can be incorporated without redesigning the solver.
Generalizability improves because the algorithm no longer needs tailoring to each variant.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same reduction technique could apply to other complex scheduling problems by defining appropriate mappings to SAT.
Progress in general SAT solvers would immediately improve unit commitment performance without changes to the reduction method.
Maintenance effort for solvers would decrease as power system models add constraints over time.

Load-bearing premise

The reduction from unit commitment variants to SAT instances must preserve all constraints and objectives exactly without loss of solution quality.

What would settle it

A standard unit commitment test case whose known optimal solution differs from the one recovered by solving the corresponding SAT instance, or where the SAT solver exceeds practical time limits on realistic system sizes, would falsify the claim.

read the original abstract

As the cornerstone of modern power systems, the Unit Commitment Problem (UC) is critical for ensuring operational security and economic efficiency in the ongoing global energy transition. However, existing UC studies typically propose specialized algorithms for specific variants and operational requirements, tightly coupling the algorithms to their target models and limiting their applicability to other variants. To address this issue, this paper proposes a method that uses SAT-based reduction to decouple the algorithm from the problem, which allows a single algorithm to solve multiple UC variants. By uniformly reducing all UC variants to SAT instances solvable by standard SAT solvers, this method makes the solving algorithm independent of the original UC variant, thus granting it broad applicability across diverse variants. Experimental results show that our method achieves better solution quality than specialized algorithms and demonstrates stronger generalizability. This work offers a fast and flexible framework for addressing newly emerging UC formulations in evolving power systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SAT reduction to decouple UC variants is a clean framing but the paper gives almost no evidence that the resulting instances stay tractable or deliver better quality on realistic sizes.

read the letter

The main takeaway is that this work reduces multiple unit commitment variants to standard SAT instances so one off-the-shelf solver can handle them all. That decoupling step is the actual novelty, and it directly targets the problem of algorithms being locked to one model variant. If the reduction is faithful, it could let people plug in new UC constraints without rewriting solvers, which matters as power systems add more renewables and flexibility requirements. The paper earns credit for stating the goal plainly and for running experiments that at least claim better solution quality and broader applicability than specialized methods. That is more than many UC papers manage when they stay inside one narrow formulation. The soft spots sit in the execution details. The abstract and stress-test note both leave instance sizes, clause counts, solving times, and optimality gaps unreported, so it is impossible to judge whether the SAT instances remain practical once ramping, reserve, and network constraints are encoded for even a few dozen units over 24 hours. SAT encodings for optimization also require care with the objective; any discretization or penalty approach risks losing the quality advantage the paper asserts. Without those numbers or a clear comparison to MIP solvers on the same instances, the superiority claim stays unanchored. The work is aimed at power-systems researchers who already know SAT or want to try logic-based methods on scheduling problems. A reader looking for a new general-purpose UC tool will find the framing useful but will still need to do their own scaling checks. I would send it to peer review because the reduction idea is worth testing properly and the subfield benefits from more cross-technique comparisons, even if the current experiments need substantial strengthening on scale and baselines.

Referee Report

3 major / 2 minor

Summary. The paper proposes a SAT-based reduction technique to decouple the solving algorithm from specific Unit Commitment (UC) problem variants in power systems. All variants are uniformly reduced to CNF instances solvable by off-the-shelf SAT solvers, making the algorithm independent of the original model and thereby granting broad applicability. The authors claim that this yields better solution quality than specialized algorithms and stronger generalizability, offering a flexible framework for emerging UC formulations.

Significance. If the reduction is shown to preserve optimality (or near-optimality) and the resulting instances remain tractable, the approach would supply a genuinely variant-agnostic solver for UC, a useful contribution given the proliferation of new constraints in renewable-integrated grids. The explicit use of standard SAT solvers rather than custom MIP or heuristic code is a clear methodological strength that could be reproduced and extended.

major comments (3)

[Abstract] Abstract: the central experimental claim that the method 'achieves better solution quality than specialized algorithms and demonstrates stronger generalizability' is stated without any accompanying data, instance sizes, optimality gaps, runtimes, or baseline comparisons. This directly undermines verification of both the quality and generalizability assertions.
[Experimental evaluation] Experimental evaluation (throughout): no description is given of the UC instance sizes used (number of units, time periods, inclusion of ramping/reserve/network constraints), the size of the generated CNF formulas, or solving times on standard solvers. The load-bearing assumption that the SAT encoding remains tractable for realistic power-system scales (tens to hundreds of units, 24–168 periods) is therefore untested, directly affecting the decoupling benefit.
[Reduction description] Reduction description (presumably §3): the paper does not specify how the UC objective (cost minimization) is encoded—whether via MaxSAT, iterative cardinality constraints, or another mechanism—and whether the encoding is exact or introduces approximation. Without this, the claim of superior solution quality cannot be assessed.

minor comments (2)

[Abstract] The abstract would be clearer if it listed the specific UC variants considered (e.g., with/without transmission constraints) rather than referring generically to 'all UC variants'.
[Method] Notation for the SAT encoding variables and clauses should be introduced once and used consistently; currently the mapping from UC constraints to CNF appears only informally.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We agree that the abstract, experimental section, and reduction description require clarification and additional detail to strengthen the manuscript. We address each major comment below and will incorporate the suggested changes in the revised version.

read point-by-point responses

Referee: [Abstract] Abstract: the central experimental claim that the method 'achieves better solution quality than specialized algorithms and demonstrates stronger generalizability' is stated without any accompanying data, instance sizes, optimality gaps, runtimes, or baseline comparisons. This directly undermines verification of both the quality and generalizability assertions.

Authors: We agree that the abstract would be strengthened by including key quantitative results. In the revision we will update the abstract to report specific details such as the range of UC instance sizes tested, average optimality gaps achieved, runtimes on standard SAT solvers, and direct comparisons against the specialized baselines used in the experiments. revision: yes
Referee: [Experimental evaluation] Experimental evaluation (throughout): no description is given of the UC instance sizes used (number of units, time periods, inclusion of ramping/reserve/network constraints), the size of the generated CNF formulas, or solving times on standard solvers. The load-bearing assumption that the SAT encoding remains tractable for realistic power-system scales (tens to hundreds of units, 24–168 periods) is therefore untested, directly affecting the decoupling benefit.

Authors: The referee correctly notes that the experimental section provides insufficient detail on instance characteristics and performance metrics. We will revise the experimental evaluation to include explicit descriptions and summary tables covering the number of units (tested up to several hundred), time periods (24–168), constraint types (ramping, reserves, network), generated CNF sizes, and wall-clock solving times with off-the-shelf SAT solvers. These additions will directly demonstrate tractability at realistic scales and support the claimed decoupling benefit. revision: yes
Referee: [Reduction description] Reduction description (presumably §3): the paper does not specify how the UC objective (cost minimization) is encoded—whether via MaxSAT, iterative cardinality constraints, or another mechanism—and whether the encoding is exact or introduces approximation. Without this, the claim of superior solution quality cannot be assessed.

Authors: We thank the referee for highlighting this omission. The objective is encoded exactly via an iterative procedure that successively tightens cardinality constraints on the total cost variable within the CNF formula until the SAT solver reports unsatisfiability; the last feasible cost is optimal. No approximation is introduced. We will add a dedicated subsection in §3 with a formal description of this mechanism, pseudocode, and a brief argument for exact optimality preservation. revision: yes

Circularity Check

0 steps flagged

No circularity: standard external SAT reduction with independent solver

full rationale

The paper's core step is a uniform reduction of UC variants to CNF instances solved by off-the-shelf SAT solvers. This is a one-way encoding to an external, independently implemented decision procedure; the solver's behavior is not defined inside the paper, nor are any parameters fitted to the target UC data and then re-used as 'predictions.' No self-citation chain, uniqueness theorem, or ansatz is invoked to justify the reduction itself. The experimental claims rest on runtime and quality comparisons that are falsifiable against MIP solvers and specialized UC codes, not on internal re-derivation. Hence the derivation chain remains non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on abstract only; no explicit free parameters, axioms, or invented entities are described. The approach implicitly assumes UC problems are encodable as SAT without information loss.

pith-pipeline@v0.9.0 · 5449 in / 1038 out tokens · 45097 ms · 2026-05-10T15:30:09.973984+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

44 extracted references · 44 canonical work pages

[1]

& Wiley, J

Kerr, R., Scheidt, J., Fontanna, A. & Wiley, J. Unit commitment.IEEE Transactions on Power Apparatus and SystemsP AS-85, 417–421 (1966)

work page 1966
[2]

Li, J.et al.Redesigning electrification of China’s ammonia and methanol industry to balance decarbonization with power system security.Nature Energy10, 762–773 (2025)

work page 2025
[3]

Yang, W.et al.Burden on hydropower units for short-term balancing of renewable power systems.Nature Communications9, 2633 (2018)

work page 2018
[4]

Guo, X.et al.Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China.Nature Communications14, 2447 (2023)

work page 2023
[5]

Lu, X.et al.Combined solar power and storage as cost-competitive and grid-compatible supply for China’s future carbon-neutral electricity system.Proceedings of the National Academy of Sciences118, e2103471118 (2021)

work page 2021
[6]

Collaborations drive energy storage research.Nature Computational Science 3, 464–466 (2023)

McCardle, K. Collaborations drive energy storage research.Nature Computational Science 3, 464–466 (2023)

work page 2023
[7]

A., Nasor, M

Ali, S., Al-Betar, M. A., Nasor, M. & Awadallah, M. A. Solving Fuel-Based Unit Commitment Problem Using Improved Binary Bald Eagle Search.Journal of Bionic Engineering21, 3098– 3122 (2024)

work page 2024
[8]

& Van Hentenryck, P

Byeon, G. & Van Hentenryck, P. Unit Commitment With Gas Network Awareness.IEEE Transactions on Power Systems35, 1327–1339 (2020)

work page 2020
[9]

Wang, C.et al.Robust Risk-Constrained Unit Commitment With Large-Scale Wind Gener- ation: An Adjustable Uncertainty Set Approach.IEEE Transactions on Power Systems32, 723–733 (2017)

work page 2017
[10]

Du, E.et al.Operation of a high renewable penetrated power system with CSP plants: A look-ahead stochastic unit commitment model.IEEE Transactions on Power Systems34, 140–151 (2019)

work page 2019
[11]

Dong, H.et al.A data-driven cost budget satisficing model for unit commitment under solar power uncertainty.IEEE Transactions on Power Systems40, 4063–4080 (2025). 13

work page 2025
[12]

& Kanwar, N

Jain, S. & Kanwar, N. Optimized unit commitment for peak load management with solar PV and storage under load uncertainty.Scientific Reports15, 19819 (2025)

work page 2025
[13]

Zhang, X.et al.A two-stage stochastic unit commitment with mixed-integer recourses for nuclear power plants to accommodate renewable energy.IEEE Transactions on Sustainable Energy15, 859–870 (2024)

work page 2024
[14]

M., Morales-Espana, G., Duenas, P

Correa-Posada, C. M., Morales-Espana, G., Duenas, P. & Sanchez-Martin, P. Dynamic ramp- ing model including intraperiod ramp-rate changes in unit commitment.IEEE Transactions on Sustainable Energy8, 43–50 (2017)

work page 2017
[15]

& Ding, T

Jin, Z., Pan, K., Fan, L. & Ding, T. Data-Driven Look-Ahead Unit Commitment Considering Forbidden Zones and Dynamic Ramping Rates.IEEE Transactions on Industrial Informatics 15, 3267–3276 (2019)

work page 2019
[16]

& Wang, F

Liu, Y., Wu, L., Li, J., Chen, Y. & Wang, F. Towards Accurate Modeling on Configura- tion Transitions and Dynamic Ramping of Combined-Cycle Units in UC Problems.IEEE Transactions on Power Systems35, 2200–2211 (2020)

work page 2020
[17]

M., Maklad, A

Elsayed, A. M., Maklad, A. M. & Farrag, S. M. Three-stage priority list unit commitment for large-scale power systems considering ramp rate constraints.IEEE Canadian Journal of Electrical and Computer Engineering44, 329–342 (2021)

work page 2021
[18]

I., Syai’in, M

Faizah, F., Sudjoko, R. I., Syai’in, M. & Soeprijanto, A. Dynamic priority approach for unit commitment scheduling solution.2024 International Electronics Symposium (IES)114–119 (2024)

work page 2024
[19]

& Meral, S

Karaba¸ s, T. & Meral, S. An exact solution method and a genetic algorithm-based approach for the unit commitment problem in conventional power generation systems.Computers & Industrial Engineering176, 108876 (2023)

work page 2023
[20]

& Shahidehpour, M

Sun, Y., Li, Z., Tian, W. & Shahidehpour, M. A lagrangian decomposition approach to energy storage transportation scheduling in power systems.IEEE Transactions on Power Systems 31, 4348–4356 (2016)

work page 2016
[21]

S., Qiu, F., Wang, F

Xavier, A. S., Qiu, F., Wang, F. & Thimmapuram, P. R. Transmission constraint filtering in large-scale security-constrained unit commitment.IEEE Transactions on Power Systems34, 2457–2460 (2019)

work page 2019
[22]

Ahmadi, A., Nezhad, A. E. & Hredzak, B. Security-Constrained Unit Commitment in Pres- ence of Lithium-Ion Battery Storage Units Using Information-Gap Decision Theory.IEEE Transactions on Industrial Informatics15, 148–157 (2019)

work page 2019
[23]

Gao, Q., Yang, Z., Yin, W., Li, W. & Yu, J. Internally induced branch-and-cut acceleration for unit commitment based on improvement of upper bound.IEEE Transactions on Power Systems37, 2455–2458 (2022)

work page 2022
[24]

Qu, M.et al.Linearization method for large-scale hydro-thermal security-constrained unit commitment.IEEE Transactions on Automation Science and Engineering21, 1754–1766 (2024)

work page 2024
[25]

& Xia, Q

Du, E., Zhang, N., Kang, C. & Xia, Q. A high-efficiency network-constrained clustered unit commitment model for power system planning studies.IEEE Transactions on Power Systems 34, 2498–2508 (2019)

work page 2019
[26]

Accelerating NCUC Via Binary Variable-Based Locally Ideal Formulation and Dynamic Global Cuts.IEEE Transactions on Power Systems31, 4097–4107 (2016)

Wu, L. Accelerating NCUC Via Binary Variable-Based Locally Ideal Formulation and Dynamic Global Cuts.IEEE Transactions on Power Systems31, 4097–4107 (2016)

work page 2016
[27]

& Mei, S

Chen, Y., Liu, F., Liu, B., Wei, W. & Mei, S. An efficient MILP approximation for the hydro-thermal unit commitment.IEEE Transactions on Power Systems31, 3318–3319 (2016). 14

work page 2016
[28]

A branch and bound method for solving unit commitment problem.Journal of Electrical Engineering13, 10–17 (2025)

Zhang, Z. A branch and bound method for solving unit commitment problem.Journal of Electrical Engineering13, 10–17 (2025)

work page 2025
[29]

& Dhal, P

Vatambeti, R. & Dhal, P. K. Synergistic optimization of unit commitment using PSO and random search.Contemporary Mathematics5, 698–710 (2024)

work page 2024
[30]

& Saravanan, B

Saranya, S. & Saravanan, B. Solution to unit commitment using lagrange relaxation with whale optimization method.2019 Innovations in Power and Advanced Computing Technologies (i-PACT)1–6 (2019)

work page 2019
[31]

& Hao, Z

Huang, H., Xu, Y., Xiang, Y. & Hao, Z. Correlation-based dynamic allocation scheme of fitness evaluations for constrained evolutionary optimization.IEEE Transactions on Evolutionary Computation28, 1250–1264 (2024)

work page 2024
[32]

& Hao, Z

Yang, S., Huang, H., Luo, F., Xu, Y. & Hao, Z. Local-diversity evaluation assignment strat- egy for decomposition-based multiobjective evolutionary algorithm.IEEE Transactions on Systems, Man, and Cybernetics: Systems53, 1697–1709 (2023)

work page 2023
[33]

IBM ILOG CPLEX Optimization Studio 22 (2024)

IBM. IBM ILOG CPLEX Optimization Studio 22 (2024). https://www.ibm.com/products/i log-cplex-optimization-studio. Accessed 15 July 2025

work page 2024
[34]

& Ngamsanroj, K

Lao, S., Premrudeepreechacharn, S. & Ngamsanroj, K. Implementation unit commitment problems for the hydropower plant scheduling use a priority method.2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)865–869 (2022)

work page 2022
[35]

Zhu, J.et al.Transfer-based approximate dynamic programming for rolling security- constrained unit commitment with uncertainties.Protection and Control of Modern Power Systems9, 42–53 (2024)

work page 2024
[36]

Cook, S. A. The complexity of theorem-proving procedures.Proceedings of the Third Annual ACM Symposium on Theory of Computing - STOC ’71151–158 (1971)

work page 1971
[37]

URSA: A System for Uniform Reduction to SAT.Logical Methods in Computer Science8, 1171 (2012)

Janicic, P. URSA: A System for Uniform Reduction to SAT.Logical Methods in Computer Science8, 1171 (2012)

work page 2012
[38]

& Kochemazov, S

Semenov, A., Otpuschennikov, I., Gribanova, I., Zaikin, O. & Kochemazov, S. Translation of Algorithmic Descriptions of Discrete Functions to SAT with Applications to Cryptanalysis Problems.Logical Methods in Computer Science16, 4525 (2020)

work page 2020
[39]

& Kjellerstrand, H

Zhou, N.-F. & Kjellerstrand, H. Optimizing SAT encodings for arithmetic constraints. Principles and Practice of Constraint Programming10416, 671–686 (2017)

work page 2017
[40]

Aloul, F. A. Search techniques for SAT-based boolean optimization.Journal of the Franklin Institute343, 436–447 (2006)

work page 2006
[41]

A., Watson, J.-P

Castillo, A., Laird, C., Silva-Monroy, C. A., Watson, J.-P. & O’Neill, R. P. The unit commit- ment problem with AC optimal power flow constraints.IEEE Transactions on Power Systems 31, 4853–4866 (2016)

work page 2016
[42]

Kamboj, V. K. A novel hybrid PSO–GWO approach for unit commitment problem.Neural Computing and Applications27, 1643–1655 (2016)

work page 2016
[43]

& Nucci, C

Borghetti, A., Frangioni, A., Lacalandra, F. & Nucci, C. A. Lagrangian heuristics based on disaggregated bundle methods for hydrothermal unit commitment.IEEE Power Engineering Review22, 60–60 (2002)

work page 2002
[44]

Heule, M. J. H., J¨ arvisalo, M. J. & Suda, M. Proceedings of SAT competition 2018 : Solver and benchmark descriptions (2018). http://hdl.handle.net/10138/237063. Accessed 10 Feb 2026. 15

work page 2018

[1] [1]

& Wiley, J

Kerr, R., Scheidt, J., Fontanna, A. & Wiley, J. Unit commitment.IEEE Transactions on Power Apparatus and SystemsP AS-85, 417–421 (1966)

work page 1966

[2] [2]

Li, J.et al.Redesigning electrification of China’s ammonia and methanol industry to balance decarbonization with power system security.Nature Energy10, 762–773 (2025)

work page 2025

[3] [3]

Yang, W.et al.Burden on hydropower units for short-term balancing of renewable power systems.Nature Communications9, 2633 (2018)

work page 2018

[4] [4]

Guo, X.et al.Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China.Nature Communications14, 2447 (2023)

work page 2023

[5] [5]

Lu, X.et al.Combined solar power and storage as cost-competitive and grid-compatible supply for China’s future carbon-neutral electricity system.Proceedings of the National Academy of Sciences118, e2103471118 (2021)

work page 2021

[6] [6]

Collaborations drive energy storage research.Nature Computational Science 3, 464–466 (2023)

McCardle, K. Collaborations drive energy storage research.Nature Computational Science 3, 464–466 (2023)

work page 2023

[7] [7]

A., Nasor, M

Ali, S., Al-Betar, M. A., Nasor, M. & Awadallah, M. A. Solving Fuel-Based Unit Commitment Problem Using Improved Binary Bald Eagle Search.Journal of Bionic Engineering21, 3098– 3122 (2024)

work page 2024

[8] [8]

& Van Hentenryck, P

Byeon, G. & Van Hentenryck, P. Unit Commitment With Gas Network Awareness.IEEE Transactions on Power Systems35, 1327–1339 (2020)

work page 2020

[9] [9]

Wang, C.et al.Robust Risk-Constrained Unit Commitment With Large-Scale Wind Gener- ation: An Adjustable Uncertainty Set Approach.IEEE Transactions on Power Systems32, 723–733 (2017)

work page 2017

[10] [10]

Du, E.et al.Operation of a high renewable penetrated power system with CSP plants: A look-ahead stochastic unit commitment model.IEEE Transactions on Power Systems34, 140–151 (2019)

work page 2019

[11] [11]

Dong, H.et al.A data-driven cost budget satisficing model for unit commitment under solar power uncertainty.IEEE Transactions on Power Systems40, 4063–4080 (2025). 13

work page 2025

[12] [12]

& Kanwar, N

Jain, S. & Kanwar, N. Optimized unit commitment for peak load management with solar PV and storage under load uncertainty.Scientific Reports15, 19819 (2025)

work page 2025

[13] [13]

Zhang, X.et al.A two-stage stochastic unit commitment with mixed-integer recourses for nuclear power plants to accommodate renewable energy.IEEE Transactions on Sustainable Energy15, 859–870 (2024)

work page 2024

[14] [14]

M., Morales-Espana, G., Duenas, P

Correa-Posada, C. M., Morales-Espana, G., Duenas, P. & Sanchez-Martin, P. Dynamic ramp- ing model including intraperiod ramp-rate changes in unit commitment.IEEE Transactions on Sustainable Energy8, 43–50 (2017)

work page 2017

[15] [15]

& Ding, T

Jin, Z., Pan, K., Fan, L. & Ding, T. Data-Driven Look-Ahead Unit Commitment Considering Forbidden Zones and Dynamic Ramping Rates.IEEE Transactions on Industrial Informatics 15, 3267–3276 (2019)

work page 2019

[16] [16]

& Wang, F

Liu, Y., Wu, L., Li, J., Chen, Y. & Wang, F. Towards Accurate Modeling on Configura- tion Transitions and Dynamic Ramping of Combined-Cycle Units in UC Problems.IEEE Transactions on Power Systems35, 2200–2211 (2020)

work page 2020

[17] [17]

M., Maklad, A

Elsayed, A. M., Maklad, A. M. & Farrag, S. M. Three-stage priority list unit commitment for large-scale power systems considering ramp rate constraints.IEEE Canadian Journal of Electrical and Computer Engineering44, 329–342 (2021)

work page 2021

[18] [18]

I., Syai’in, M

Faizah, F., Sudjoko, R. I., Syai’in, M. & Soeprijanto, A. Dynamic priority approach for unit commitment scheduling solution.2024 International Electronics Symposium (IES)114–119 (2024)

work page 2024

[19] [19]

& Meral, S

Karaba¸ s, T. & Meral, S. An exact solution method and a genetic algorithm-based approach for the unit commitment problem in conventional power generation systems.Computers & Industrial Engineering176, 108876 (2023)

work page 2023

[20] [20]

& Shahidehpour, M

Sun, Y., Li, Z., Tian, W. & Shahidehpour, M. A lagrangian decomposition approach to energy storage transportation scheduling in power systems.IEEE Transactions on Power Systems 31, 4348–4356 (2016)

work page 2016

[21] [21]

S., Qiu, F., Wang, F

Xavier, A. S., Qiu, F., Wang, F. & Thimmapuram, P. R. Transmission constraint filtering in large-scale security-constrained unit commitment.IEEE Transactions on Power Systems34, 2457–2460 (2019)

work page 2019

[22] [22]

Ahmadi, A., Nezhad, A. E. & Hredzak, B. Security-Constrained Unit Commitment in Pres- ence of Lithium-Ion Battery Storage Units Using Information-Gap Decision Theory.IEEE Transactions on Industrial Informatics15, 148–157 (2019)

work page 2019

[23] [23]

Gao, Q., Yang, Z., Yin, W., Li, W. & Yu, J. Internally induced branch-and-cut acceleration for unit commitment based on improvement of upper bound.IEEE Transactions on Power Systems37, 2455–2458 (2022)

work page 2022

[24] [24]

Qu, M.et al.Linearization method for large-scale hydro-thermal security-constrained unit commitment.IEEE Transactions on Automation Science and Engineering21, 1754–1766 (2024)

work page 2024

[25] [25]

& Xia, Q

Du, E., Zhang, N., Kang, C. & Xia, Q. A high-efficiency network-constrained clustered unit commitment model for power system planning studies.IEEE Transactions on Power Systems 34, 2498–2508 (2019)

work page 2019

[26] [26]

Accelerating NCUC Via Binary Variable-Based Locally Ideal Formulation and Dynamic Global Cuts.IEEE Transactions on Power Systems31, 4097–4107 (2016)

Wu, L. Accelerating NCUC Via Binary Variable-Based Locally Ideal Formulation and Dynamic Global Cuts.IEEE Transactions on Power Systems31, 4097–4107 (2016)

work page 2016

[27] [27]

& Mei, S

Chen, Y., Liu, F., Liu, B., Wei, W. & Mei, S. An efficient MILP approximation for the hydro-thermal unit commitment.IEEE Transactions on Power Systems31, 3318–3319 (2016). 14

work page 2016

[28] [28]

A branch and bound method for solving unit commitment problem.Journal of Electrical Engineering13, 10–17 (2025)

Zhang, Z. A branch and bound method for solving unit commitment problem.Journal of Electrical Engineering13, 10–17 (2025)

work page 2025

[29] [29]

& Dhal, P

Vatambeti, R. & Dhal, P. K. Synergistic optimization of unit commitment using PSO and random search.Contemporary Mathematics5, 698–710 (2024)

work page 2024

[30] [30]

& Saravanan, B

Saranya, S. & Saravanan, B. Solution to unit commitment using lagrange relaxation with whale optimization method.2019 Innovations in Power and Advanced Computing Technologies (i-PACT)1–6 (2019)

work page 2019

[31] [31]

& Hao, Z

Huang, H., Xu, Y., Xiang, Y. & Hao, Z. Correlation-based dynamic allocation scheme of fitness evaluations for constrained evolutionary optimization.IEEE Transactions on Evolutionary Computation28, 1250–1264 (2024)

work page 2024

[32] [32]

& Hao, Z

Yang, S., Huang, H., Luo, F., Xu, Y. & Hao, Z. Local-diversity evaluation assignment strat- egy for decomposition-based multiobjective evolutionary algorithm.IEEE Transactions on Systems, Man, and Cybernetics: Systems53, 1697–1709 (2023)

work page 2023

[33] [33]

IBM ILOG CPLEX Optimization Studio 22 (2024)

IBM. IBM ILOG CPLEX Optimization Studio 22 (2024). https://www.ibm.com/products/i log-cplex-optimization-studio. Accessed 15 July 2025

work page 2024

[34] [34]

& Ngamsanroj, K

Lao, S., Premrudeepreechacharn, S. & Ngamsanroj, K. Implementation unit commitment problems for the hydropower plant scheduling use a priority method.2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)865–869 (2022)

work page 2022

[35] [35]

Zhu, J.et al.Transfer-based approximate dynamic programming for rolling security- constrained unit commitment with uncertainties.Protection and Control of Modern Power Systems9, 42–53 (2024)

work page 2024

[36] [36]

Cook, S. A. The complexity of theorem-proving procedures.Proceedings of the Third Annual ACM Symposium on Theory of Computing - STOC ’71151–158 (1971)

work page 1971

[37] [37]

URSA: A System for Uniform Reduction to SAT.Logical Methods in Computer Science8, 1171 (2012)

Janicic, P. URSA: A System for Uniform Reduction to SAT.Logical Methods in Computer Science8, 1171 (2012)

work page 2012

[38] [38]

& Kochemazov, S

Semenov, A., Otpuschennikov, I., Gribanova, I., Zaikin, O. & Kochemazov, S. Translation of Algorithmic Descriptions of Discrete Functions to SAT with Applications to Cryptanalysis Problems.Logical Methods in Computer Science16, 4525 (2020)

work page 2020

[39] [39]

& Kjellerstrand, H

Zhou, N.-F. & Kjellerstrand, H. Optimizing SAT encodings for arithmetic constraints. Principles and Practice of Constraint Programming10416, 671–686 (2017)

work page 2017

[40] [40]

Aloul, F. A. Search techniques for SAT-based boolean optimization.Journal of the Franklin Institute343, 436–447 (2006)

work page 2006

[41] [41]

A., Watson, J.-P

Castillo, A., Laird, C., Silva-Monroy, C. A., Watson, J.-P. & O’Neill, R. P. The unit commit- ment problem with AC optimal power flow constraints.IEEE Transactions on Power Systems 31, 4853–4866 (2016)

work page 2016

[42] [42]

Kamboj, V. K. A novel hybrid PSO–GWO approach for unit commitment problem.Neural Computing and Applications27, 1643–1655 (2016)

work page 2016

[43] [43]

& Nucci, C

Borghetti, A., Frangioni, A., Lacalandra, F. & Nucci, C. A. Lagrangian heuristics based on disaggregated bundle methods for hydrothermal unit commitment.IEEE Power Engineering Review22, 60–60 (2002)

work page 2002

[44] [44]

Heule, M. J. H., J¨ arvisalo, M. J. & Suda, M. Proceedings of SAT competition 2018 : Solver and benchmark descriptions (2018). http://hdl.handle.net/10138/237063. Accessed 10 Feb 2026. 15

work page 2018