AC/DC Intensive Scheduling of Offshore Wind Farm Clusters Under Uncertainty: A Confidence Gap Decision Theory Approach

The large-scale grid integration of offshore wind farm clusters presents significant operational challenges due to the inherent uncertainty of wind power output and the high capital cost of AC/DC hybrid transmission infrastructure. This paper proposes an intensive optimization scheduling strategy for offshore wind farm clusters based on confidence gap decision theory (CGDT). Firstly, the proposed approach employs an equivalent single-unit aggregation model to characterize the collective output of wind farm clusters. Secondly, a multi-objective deterministic dispatching model for AC/DC intensive network configuration is constructed, aiming to maximize wind power accommodation, power supply proportion and intensive utilization of transmission corridors. Furthermore, kernel density estimation is adopted to fit the prediction error distribution of wind power output for describing the uncertainty of offshore wind power output, and an uncertain robust optimization model is developed and solved on the basis of CGDT. Finally, taking an AC/DC system of a certain offshore wind farm cluster as a case study, simulation analysis is conducted on the dispatching performance of the proposed method and the impacts of parameters on dispatching results. The results demonstrate that both the mean values and stability of dispatching indices of the proposed method are superior to those of traditional methods. The balance between dispatching robustness and economy can be achieved by reasonably setting the target significance level. The proposed strategy can provide a theoretical foundation for the intensive optimal AC/DC dispatching of offshore wind farm clusters.