Modeling Dynamic Spatial Correlations of Geographically Distributed Wind Farms and Constructing Ellipsoidal Uncertainty Sets for Optimization-Based Generation Scheduling


The correlation data is terribly necessary for system operations with geographically distributed wind farms, and necessary for optimization-primarily based generation scheduling ways such as the strong optimization (RO). The purpose of this paper is to supply the dynamic spatial correlations between the geographically distributed wind farms and apply them to model the ellipsoidal uncertainty sets for the robust unit commitment model. A stochastic dynamic system is established for the distributed wind farms primarily based on a mesoscale numerical weather prediction (NWP) model, wind speed downscaling, and wind power curve models. By combining the observed wind generation measurements, a dynamic backtracking framework based mostly on the extended Kalman filter is applied to predict the wind generation and the dynamic spatial correlations for the wind farms. In case studies, the new method is tested on actual wind farms and compared with the Gaussian copula methodology. The testing results validate the effectiveness of the new method. It's shown that the new method can offer additional favorable interval forecasts for the mixture wind generation than the Gaussian copula technique in the complete forecast horizon, and by using the expected spatial correlations, we have a tendency to can get more correct ellipsoidal uncertainty sets than the Gaussian copula method and the frequently used budget uncertainty set (BUS).

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