A Clustering-Based Approach for Wind Farm Placement in Radial Distribution Systems Considering Wake Effect and a Time-Acceleration Constraint

This article proposes a method based on data clustering for the optimal placement and sizing of wind farms (WFs) in radial distribution systems considering uncertainty wake effect. The stochastic output turbines makes optimization problem more challenging. Using probabilistic methods such analyses proves efficient to yield reliable results. clustering-based Monte Carlo simulation is used bunch powers farm into clusters. network loss voltage profile are then evaluated achieve candidate bus WF with number turbines. To accelerate procedure, technical constraint so as eliminate nonimportant samples size evaluation, which results reduction computational burden. In addition, two load flow calculations investigated, namely direct indirect backward/forward sweep evaluate time burden proposed problem. effectiveness methodology illustated by conducting case studies.