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• W2795859926 abstract "To incorporate the superiority of both stochastic and robust approaches, a data-driven stochastic optimization is employed to solve the security-constrained unit commitment model. This approach makes the most use of the historical data to generate a set of possible probability distributions for wind power outputs and then it optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To tackle this issue, a duality-free decomposition method is proposed in this paper. This approach does not require doing duality, which can save a large set of dual variables and constraints, and therefore reduces the computational burden. In addition, the inner max-min problem has a special mathematical structure, where the scenarios have the similar constraint. Thus, the max-min problem can be decomposed into independent subproblems to be solved in parallel, which further improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal." @default.
• W2795859926 created "2018-04-13" @default.
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• W2795859926 date "2019-01-01" @default.
• W2795859926 modified "2023-10-17" @default.
• W2795859926 title "Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment" @default.
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• W2795859926 doi "https://doi.org/10.1109/tste.2018.2825361" @default.
• W2795859926 hasPublicationYear "2019" @default.
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