Sequential Lagrangian-MILP Approaches for Unit Commitment Problems (Articolo in rivista)

Type
Label
  • Sequential Lagrangian-MILP Approaches for Unit Commitment Problems (Articolo in rivista) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Alternative label
  • Frangioni A.; Gentile C.; Lacalandra F. (2011)
    Sequential Lagrangian-MILP Approaches for Unit Commitment Problems
    in International journal of electrical power & energy systems
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Frangioni A.; Gentile C.; Lacalandra F. (literal)
Pagina inizio
  • 585 (literal)
Pagina fine
  • 593 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 33 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Frangioni Antonio, Università di Pisa, Dipartimento di Informatica Lacalandra Fabrizio, fabrizio.lacalandra@fastwebnet.i (literal)
Titolo
  • Sequential Lagrangian-MILP Approaches for Unit Commitment Problems (literal)
Abstract
  • The short-term Unit Commitment (UC) problem in hydro-thermal power generation is a fundamental problem in short-term electrical generation scheduling. Historically, Lagrangian techniques have been used to tackle this large-scale, difficult Mixed-Integer NonLinear Program (MINLP); this requires being able to efficiently solve the Lagrangian subproblems, which has only recently become possible (efficiently enough) for units subject to significant ramp constraints. In the last years, alternative approaches have been devised where the nonlinearities in the problem are approximated by means of piecewise-linear functions, so that UC can be approximated by a Mixed-Integer Linear Program (MILP); in particular, using a recently developed class of \emph{valid inequalities} for the problem, called ``Perspective Cuts'', significant improvements have been obtained in the efficiency and effectiveness of the solution algorithms. These two different approaches have complementary strengths; Lagrangian ones provide very good lower bounds quickly, but they require sophisticated heuristics---which may need to be changed every time that the mathematical model changes---for producing actual feasible solutions. MILP approaches have been shown to be able to provide very good feasible solutions quickly, but their lower bound is significantly worse. We present a sequential approach which combines the two methods, trying to exploit each one's strengths; we show, by means of extensive computational experiments on realistic instances, that the sequential approach may exhibit significantly better efficiency than either of the two basic ones, depending on the degree of accuracy requested to the feasible solutions. (literal)
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