Progressive hedging for stochastic programs with cross-scenario inequality constraints

  • Ellen Krohn AasgårdEmail author
  • Hans Ivar Skjelbred
Original Paper


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.


Progressive hedging Stochastic programming Hydropower Unit commitment Electricity auctions 



This work was supported by the Research Council of Norway under Project Number 255100/E20 MultiSharm.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.SINTEF Energy ResearchTrondheimNorway

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