TY - JOUR
T1 - Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment
AU - Ding, Tao
AU - Yang, Qingrun
AU - Liu, Xiyuan
AU - Huang, Can
AU - Yang, Yongheng
AU - Wang, Min
AU - Blaabjerg, Frede
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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.
KW - Data-driven stochastic optimization
KW - duality-free decomposition
KW - security-constrained unit commitment
KW - distributionally robust optimization
UR - http://www.scopus.com/inward/record.url?scp=85045349838&partnerID=8YFLogxK
U2 - 10.1109/TSTE.2018.2825361
DO - 10.1109/TSTE.2018.2825361
M3 - Journal article
SN - 1949-3029
VL - 10
SP - 82
EP - 93
JO - I E E E Transactions on Sustainable Energy
JF - I E E E Transactions on Sustainable Energy
IS - 1
M1 - 8334604
ER -