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

• 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3101569

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.