robust optimization problems are conventionally solved by reformulation as non-robust problems. We propose a direct method to separate split cuts for robustmixed-integer programs with polyhedral uncertainty sets. The...
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robust optimization problems are conventionally solved by reformulation as non-robust problems. We propose a direct method to separate split cuts for robustmixed-integer programs with polyhedral uncertainty sets. The method generalizes the well-known cutting plane procedure of Balas. Computational experiments show that applying cutting planes directly is favorable to the reformulation approach. It is thus viable to solve robust MIP problems in a branch-and-cut framework using a generalized linear programming oracle. (C) 2012 Elsevier B.V. All rights reserved.
robust Unit Commitment (UC) model has been intensively investigated as an effective approach to hedge against randomness and risks. All existing robust UC formulations consider uncertainties in demand and/or cost. We ...
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ISBN:
(纸本)9781509032709
robust Unit Commitment (UC) model has been intensively investigated as an effective approach to hedge against randomness and risks. All existing robust UC formulations consider uncertainties in demand and/or cost. We observe that, nevertheless, a power system could be seriously affected by surrounding temperature and there is a strong relationship among the efficiency of gas generators, demand and temperature. With that observation, we develop a robust optimization model considering correlated uncertainties in temperature and demand forecasting, and the impact of the former one on generating efficiency. Numerical experiments are conducted on a typical IEEE test system to analyse our formulation and the impact of uncertain temperature.
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