Coordination Of Wind Farm And Pumped-Storage Hydro For A Self-Healing Power Grid
Keywords
Adaptive robust optimization; column-and-constraint generation; mixed-integer linear programming; pumped-storage hydro; self-healing; wind uncertainty
Abstract
The increasing penetration of wind energy poses great challenges to the operation of power systems in normal and emergency states. However, energy storage technologies can help accommodate wind power uncertainty and variability due to their flexible characteristics. This paper focuses on the restoration phase, and provides a novel coordination strategy of wind and pumped-storage hydro (PSH) units for a faster and reliable self-healing process. The wind-PSH assisted power system restoration is formulated as a two-stage adaptive robust optimization problem. The first-stage problem determines the start-up sequence of generators and the energization times of transmission paths; and the second-stage problem decides load pickup sequences, wind power dispatch levels, and PSH units' operating modes. The column-and-constraint generation decomposition algorithm is applied to solve the two-stage adaptive robust optimization problem, which has a mixed-integer optimization in the inner-level problem. The developed coordination strategy is tested on the modified IEEE 39-bus system. Numerical results demonstrate that the coordinated wind and PSH units can increase the total energy served, enhance wind power dispatchability, and reduce wind power curtailment.
Publication Date
10-1-2018
Publication Title
IEEE Transactions on Sustainable Energy
Volume
9
Issue
4
Number of Pages
1910-1920
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/TSTE.2018.2819133
Copyright Status
Unknown
Socpus ID
85044346196 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85044346196
STARS Citation
Golshani, Amir; Sun, Wei; Zhou, Qun; Zheng, Qipeng P.; and Wang, Jianhui, "Coordination Of Wind Farm And Pumped-Storage Hydro For A Self-Healing Power Grid" (2018). Scopus Export 2015-2019. 9076.
https://stars.library.ucf.edu/scopus2015/9076