| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 398789 | International Journal of Electrical Power & Energy Systems | 2014 | 9 Pages |
•Two novel and efficient hybrid algorithms to solve the unit commitment problem.•Optimal solution for all instances of a widely used benchmark tested.•A computational analysis showing the effectiveness of the methods.
This paper presents two new solution approaches capable of finding optimal solutions for the thermal unit commitment problem in power generation planning. The approaches explore the concept of “matheuristics”, a term usually used to refer to an optimization algorithm that hybridizes (meta)heuristics with mixed integer programming solvers, in order to speed up convergence to optimality for large scale instances. Two algorithms are proposed: “local branching”, and an hybridization of particle swarm optimization with a mixed integer programming solver.From extensive computational tests on a broad set of benchmarks, the algorithms were found to be able to solve large instances. Optimal solutions were obtained for several well-known situations with dramatic reductions in CPU time for the larger cases, when compared to previously proposed exact methods.
