A Two-Stage Distributionally Robust Optimization Model for Wind Farms and Storage Units Jointly Operated Power Systems

To explore the benefit of energy storage for countering high-level wind power fluctuations, a two-stage distributionally robust optimization model is proposed for wind farms and storage units (SUs) jointly operated power systems. First, the 1-norm and \infty -norm confidence sets are presented to m...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.111132-111142
Main Authors: Li, Panpan, Song, Liangyun, Qu, Jixian, Huang, Yuehui, Wu, Xiaoyun, Lu, Xi, Xia, Shiwei
Format: Article
Language:English
Subjects:
Algorithms
CCG algorithm
Confidence intervals
confidence set
Cost analysis
Dispatching
Distributionally robust optimization
Energy storage
Generators
Load modeling
Load shedding
Optimization
Optimization models
Power systems
Robustness
Storage units
two-stage
Wind farms
Wind power
Wind power generation
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Summary:To explore the benefit of energy storage for countering high-level wind power fluctuations, a two-stage distributionally robust optimization model is proposed for wind farms and storage units (SUs) jointly operated power systems. First, the 1-norm and \infty -norm confidence sets are presented to model the fluctuations of wind power output, then a two-stage distributionally optimization model is formed to minimize system total cost with secure operation constraints, where the ON-OFF status of generators and SUs are determined in the first stage by day-ahead dispatching, while the power output of generators, wind power curtailment, load shedding and SUs charging/discharging power are optimized in the second stage. Afterward, the column-and-constraint generation (CCG) algorithm is presented to solve the proposed two-stage model. Finally the influence of confidence level of confidence sets and SU capacities on system total cost is analyzed, and the effectiveness of the proposed model is also validated by the case studies.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3101569