Cristin-resultat-ID: 1763341
Sist endret: 11. februar 2020 14:51
NVI-rapporteringsår: 2019
Vitenskapelig artikkel

Progressive hedging for stochastic programs with cross-scenario inequality constraints

  • Ellen Krohn Aasgård og
  • Hans Ivar Skjelbred


Computational Management Science
ISSN 1619-697X
e-ISSN 1619-6988
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2019
Publisert online: 2019
Trykket: 2020
Volum: 17
Hefte: 1
Sider: 141 - 160
Open Access


Scopus-ID: 2-s2.0-85075122291

Beskrivelse Beskrivelse


Progressive hedging for stochastic programs with cross-scenario inequality constraints


In this paper, we show how progressive hedging may be used to solve stochastic programming problems that involve cross-scenario inequality constraints. In contrast, standard stochastic programs involve cross-scenario equality constraints that describe the non-anticipative nature of the optimal solution. The standard progressive hedging algorithm (PHA) iteratively manipulates the objective function coefficients of the scenario subproblems to reflect the costs of non-anticipativity and penalize deviations from a non-anticipative, aggregated solution. Our proposed algorithm follows the same principle, but works with cross-scenario inequality constraints. Specifically, we focus on the problem of determining optimal bids for hydropower producers that participate in wholesale electricity auctions. The cross-scenario inequality constraints arise from the fact that bids are required to be non-decreasing. We show that PHA for inequality constraints have the same convergence properties as standard PHA, and illustrate our algorithm with results for an instance of the hydropower bidding problem.


Ellen Krohn Aasgård

  • Tilknyttet:
    ved Institutt for industriell økonomi og teknologiledelse ved Norges teknisk-naturvitenskapelige universitet

Hans Ivar Skjelbred

  • Tilknyttet:
    ved Energisystemer ved SINTEF Energi AS
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