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 sub-problems 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.
Original language | English |
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Article number | 8334604 |
Journal | I E E E Transactions on Sustainable Energy |
Volume | 10 |
Issue number | 1 |
Pages (from-to) | 82-93 |
Number of pages | 12 |
ISSN | 1949-3029 |
DOIs | |
Publication status | Published - Jan 2019 |
Keywords
- Data-driven stochastic optimization
- duality-free decomposition
- security-constrained unit commitment
- distributionally robust optimization