Probabilistic assessment of available transfer capability considering spatial correlation in wind power integrated system

With the increasing integration of wind farms, modification of current tools for evaluating and managing power systems such as available transfer capability (ATC) becomes an important issue. This study presents a computationally accurate and efficient method in evaluating ATC with large amount of uncertainty based on Latin hypercube sampling (LHS) and scenario clustering techniques. LHS is used in Monte Carlo simulation to select a system state with high sampling efficiency and good precision. Cholesky decomposition is combined into the sampling process to deal with the dependencies among input random variables. The sampled scenarios are clustered by vector quantification clustering algorithm, which contributes to the fast calculation of ATC evaluation for numerous scenarios. Finally, a sensitivity method based on optimal power flow is proposed for the clustered scenarios. The case studies, with the IEEE reliability test system, illustrate the advantages of the proposed method that largely reduces the computation burden under the premise of ensuring its accuracy. The results also verify the obvious enhancement of spatially correlated wind power on the volatility of ATC.