A new merit function to accommodate high wind power penetration of WGRs (wind generating resources)

• A new merit function to find optimal wind farm sites is proposed.
• The Kriged Wind Farm-SMES hybrid model is proposed.
• Empirical wind farm output data (ERCOT CREZ) is used to study spatial analysis and modeling.
• Visualization of predicted power outputs of new wind farm sites in Texas is suggested.

In this paper, we propose a new merit function to provide practical information to find optimal wind farm locations and projects based on spatial wind farm output prediction, including correlation with other wind farms. Our approach can predict what will happen when a new wind farm is added at various locations. Spatial power prediction of geographically distributed wind farms and their statistics through the proposed prediction model based on Kriging techniques will be presented. Using the proposed prediction model of wind generating resources, the performance of the spatial merit function in the context of the McCamey areas of ERCOT (Electric Reliability Council of Texas) will be provided. A new merit function through the prediction of wind farm outputs can play a key role to accommodate high wind power penetration from spatially distributed wind farms in power system planning models. In addition, we also propose the Kriged Wind Farm-SMES (Superconducting Magnetic Energy Storage System) hybrid model to enhance the higher wind power penetration levels using a new merit function. The proposed merit function requires only the existing data of wind generating resources around the candidate wind farm sites in order to reduce the time and costs for the installation of an anemometry tower as a practical method of wind resource assessment study compared to MCP (Measure-Correlate-Predict).