Atmospheric circulation archetypes as clustering criteria for wind power inputs into probabilistic power flow analysis

The variability of the wind resource is the primary challenge associated with the introduction of large scale wind power onto electricity networks. In addressing uncertainties associated with wind power, probabilistic power flow analysis (PPF) is often used in resolving current or future system states. These simulations are informed by input scenarios - i.e. time series that represent conditions being simulated. Thereby defining appropriate input scenarios is a critically important part of the process. This study proposes a novel methodology for clustering wind power time series based on the dominant concurrent atmospheric circulation patterns. These patterns are classified into generalized architypes using Self Organizing Maps. Thereby the probabilistic properties and data dependency structures of wind farms are approximated at the hand of the causative weather phenomena. It was found that this methodology resulted in significant variations in the probabilistic properties of wind power time series and the correlations between wind generators. It is anticipated that this methodology could be effectively applied in defining the input characteristics into operational scenarios within a PPF analysis, and inform correlations between wind farms in spatiotemporal probabilistic forecasts.