Flexible Low-Carbon Multi-objective Optimization Strategy for Wind Power Systems Considering Source and Load Prediction Information

The high proportion of wind power integration increases the uncertainty of power system operation. Sufficient flexibility is crucial for safe and reliable system operation. Meanwhile, thermal power plants play an important role in reducing carbon emissions. In this paper, a flexible low-carbon multi-objective optimization strategy is proposed that considers renewable energy and load prediction information. Firstly, based on the wind power prediction interval, a wind power random scenario set that can represent flexible adjustment requirements is constructed to optimize system flexibility and reduce model complexity. Considering the comprehensive operating characteristics of conventional thermal power units and carbon capture plants, the evaluation indicators for flexibility resources and system operation flexibility are established. These indicators can quantify the flexible adjustment ability of systems containing multiple flexibility resources. Furthermore, with the objective of economy and flexibility, a flexible low-carbon multi-objective optimization scheduling model is established to achieve the coordinated optimization of flexibility, economy, and robustness. Finally, case studies are conducted on the IEEE 39-bus system to verify the feasibility and effectiveness of the proposed method.