Optimal wind clustering methodology for electrical network adequacy studies using non sequential Monte Carlo simulation

In this paper, several clustering methodologies are investigated in order to group together wind parks with close statistical behaviour. The proposed approach is practically founded on a fast incremental algorithm. The latter requires the definition of an objective function which is based in the present case on the definition of a Pearson correlation coefficient level. The advantage of such a clustering methodology is mainly perceptible in large scale electrical systems with increased wind penetration. Indeed, it allows to group together highly correlated wind parks into the same cluster and to integrate them in a realistic way into a non sequential Monte Carlo adequacy evaluation process. Here, the proposed clustering methodology is applied to 94 wind sites located in Occidental Europe. Then, in order to point out the efficiency of the proposed clustering methodology from the wind speed sampling point of view, an adequacy study is applied to the Roy Billinton Test System in the particular case of a single wind cluster.