Reactive Power Optimization of Substation Based on Curve Clustering

Based on the annual load curve data from the substation, the reactive power demand is determined. The application of clustering algorithms is employed to group the annual demand curves, and the total compensating capacity is determined based on the maximum value within these curves. By optimizing the coverage of clustered curves with stepped capacitor curves, the goal is to minimize the area of reactive power mismatch. Additionally, the introduction of the maximum reactive power mismatch, corresponding to the minimum overall reactive power mismatch, guides the configuration capacity of static reactive power generators. A multi-objective reactive power optimization model is established with the objectives of minimizing the mismatch area and investment costs. The model is solved using Particle Swarm Optimization (PSO) algorithm to guide the reactive power compensation planning for the substation. Finally, the effectiveness of the proposed method is validated through a practical case study.