TY - JOUR
T1 - A Fast lp-based approach for robust dynamic economic dispatch problem
T2 - A feasible region projection method
AU - Liu, Yikui
AU - Wu, Lei
AU - Li, Jie
N1 - Funding Information:
Manuscript received January 3, 2020; revised May 4, 2020; accepted June 17, 2020. Date of publication June 22, 2020; date of current version August 24, 2020. This work was supported by the U.S. National Science Foundation under Grants CNS-1915756 and CMMI-1906780. Paper no. PESL-00003-2020. (Corresponding author: Lei Wu.) Yikui Liu and Lei Wu are with the ECE Department, Stevens Institute of Technology, Hoboken, NJ 07030 USA (e-mail: yliu262@stevens.edu; lei.wu@stevens.edu).
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - The robust optimization based dynamic economic dispatch (DED) model has been extensively studied to address uncertainties of net loads induced by renewable energy resource variabilities and demand fluctuations. However, the robust DED model is computationally expensive, when solved by the column-And-constraint generation (CCG) approach that iterates between a master problem and a max-min subproblem. This letter proposes a feasible region projection-based approach to equivalently reformulate the robust DED as a single-level linear programming (LP) model that can be effectively solved while guaranteeing solution optimality. Numerical studies show the proposed approach is one order of magnitude faster than CCG.
AB - The robust optimization based dynamic economic dispatch (DED) model has been extensively studied to address uncertainties of net loads induced by renewable energy resource variabilities and demand fluctuations. However, the robust DED model is computationally expensive, when solved by the column-And-constraint generation (CCG) approach that iterates between a master problem and a max-min subproblem. This letter proposes a feasible region projection-based approach to equivalently reformulate the robust DED as a single-level linear programming (LP) model that can be effectively solved while guaranteeing solution optimality. Numerical studies show the proposed approach is one order of magnitude faster than CCG.
UR - http://www.scopus.com/inward/record.url?scp=85090109733&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090109733&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2020.3004058
DO - 10.1109/TPWRS.2020.3004058
M3 - Article
AN - SCOPUS:85090109733
VL - 35
SP - 4116
EP - 4119
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 5
M1 - 9122454
ER -