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• W1990994993 abstract "We present a parallel implementation of Lagrangian relaxation for solving stochastic unit commitment subject to uncertainty in renewable power supply and generator and transmission line failures. We describe a scenario selection algorithm inspired by importance sampling in order to formulate the stochastic unit commitment problem and validate its performance by comparing it to a stochastic formulation with a very large number of scenarios, that we are able to solve through parallelization. We examine the impact of narrowing the duality gap on the performance of stochastic unit commitment and compare it to the impact of increasing the number of scenarios in the model. We report results on the running time of the model and discuss the applicability of the method in an operational setting." @default.
• W1990994993 created "2016-06-24" @default.
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• W1990994993 date "2015-05-01" @default.
• W1990994993 modified "2023-09-25" @default.
• W1990994993 title "Applying High Performance Computing to Transmission-Constrained Stochastic Unit Commitment for Renewable Energy Integration" @default.
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• W1990994993 doi "https://doi.org/10.1109/tpwrs.2014.2341354" @default.
• W1990994993 hasPublicationYear "2015" @default.
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