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