Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 10, Issue 1, Pages 82-93Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2018.2825361
Keywords
Data-driven stochastic optimization; duality-free decomposition; security-constrained unit commitment; distributionally robust optimization
Categories
Funding
- National Key Research and Development Program of China [2016YFB0901900]
- National Natural Science Foundation of China [51607137]
- China Postdoctoral Science Foundation [2017T100748]
- Project of State Key Laboratory of Electrical Insulation
- Power Equipment in Xi'an Jiaotong University [EIPE16301]
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344, LLNL-JRNL-749680]
Ask authors/readers for more resources
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 themost 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available