Multiobjective Reactive Power Optimization of Renewable Energy Power Plants Based on Time-and-Space Grouping Method

Abstract

<jats:p>The large-scale renewable energy power plants connected to a weak grid may cause bus voltage fluctuations in the renewable energy power plant and even power grid. Therefore, reactive power compensation is demanded to stabilize the bus voltage and reduce network loss. For this purpose, time-series characteristics of renewable energy power plants are firstly reflected using K-means++ clustering method. The time group behaviors of renewable energy power plants, spatial behaviors of renewable energy generation units, and a time-and-space grouping model of renewable energy power plants are thus established. Then, a mixed-integer optimization method for reactive power compensation in renewable energy power plants is developed based on the second-order cone programming (SOCP). Accordingly, power flow constraints can be simplified to achieve reactive power optimization more efficiently and quickly. Finally, the feasibility and economy for the proposed method are verified by actual renewable energy power plants.</jats:p>

Dynamic Equivalent Modeling of a Large Renewable Power Plant Using a Data-Driven Degree of Similarity Method

“…[9] Time-and-Space grouping Ref. [23] Wind Farm K-means, multilevel modeling Ref. [12] Stochastic security constrained Ref.…”

“…An intriguing advancement in this field involves the application of novel methods for clusters of renewable generation units. Some algorithms like the K-means algorithm [23], density peak clustering algorithm [24], and fuzzy clustering algorithm [25,26] have been recently employed in [23][24][25][26]. These algorithms have unveiled fresh perspectives on the processing and clustering of generation units, offering new insights and potential avenues for refining the multi-unit dynamic equivalence approach for renewable power plants.…”