Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 30, Issue 3, Pages 1109-1120Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2014.2341354
Keywords
Lagrangian relaxation; parallel computing; scenario selection; stochastic optimization; unit commitment
Categories
Funding
- National Science Foundation [IIP 0969016]
- U.S. Department of Energy through the Future Grid initiative
- Lawrence Livermore National Laboratory
- Directorate For Engineering
- Div Of Industrial Innovation & Partnersh [0969016] Funding Source: National Science Foundation
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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.
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