TY - JOUR
T1 - Robust transmission constrained unit commitment
T2 - A column merging method
AU - Li, Xuan
AU - Zhai, Qiaozhu
AU - Guan, Xiaohong
N1 - Publisher Copyright:
© 2019 The Institution of Engineering and Technology.
PY - 2020/8/3
Y1 - 2020/8/3
N2 - With the rapid integration of power sources with uncertainty, robustness must be carefully considered in the transmission constrained unit commitment (TCUC) problem. The overall computational complexity of the robust TCUC methods is closely related to the vertex number of the uncertainty set. The vertex number is further associated with: (i) the period number in the scheduling horizon, (ii) the number of nodes with uncertain power injections. In this study, a column merging method (CMM) is proposed to reduce the computational burden by merging the uncertain nodes, while still guaranteeing the robustness of the solution. By the CMM, the transmission constraints are modified, with the parameters obtained based on an analytical solution of a uniform approximation problem, so that the computational time for obtaining the modified constraints is negligible. The CMM is applied under a greedy-algorithm-based framework, where the number of merged nodes and the approximation error can be well balanced. The CMM is designed as a preprocessing tool to improve the solution efficiency for robust TCUC problems and is compatible with many solution methods (like two-stage and multi-stage robust optimisation methods). Numerical tests show the method is effective.
AB - With the rapid integration of power sources with uncertainty, robustness must be carefully considered in the transmission constrained unit commitment (TCUC) problem. The overall computational complexity of the robust TCUC methods is closely related to the vertex number of the uncertainty set. The vertex number is further associated with: (i) the period number in the scheduling horizon, (ii) the number of nodes with uncertain power injections. In this study, a column merging method (CMM) is proposed to reduce the computational burden by merging the uncertain nodes, while still guaranteeing the robustness of the solution. By the CMM, the transmission constraints are modified, with the parameters obtained based on an analytical solution of a uniform approximation problem, so that the computational time for obtaining the modified constraints is negligible. The CMM is applied under a greedy-algorithm-based framework, where the number of merged nodes and the approximation error can be well balanced. The CMM is designed as a preprocessing tool to improve the solution efficiency for robust TCUC problems and is compatible with many solution methods (like two-stage and multi-stage robust optimisation methods). Numerical tests show the method is effective.
UR - https://www.scopus.com/pages/publications/85089940242
U2 - 10.1049/iet-gtd.2018.6314
DO - 10.1049/iet-gtd.2018.6314
M3 - 文章
AN - SCOPUS:85089940242
SN - 1751-8687
VL - 14
SP - 2968
EP - 2975
JO - IET Generation, Transmission and Distribution
JF - IET Generation, Transmission and Distribution
IS - 15
ER -