Unified probabilistic modeling of wind reserves for demand response and frequency regulation in islanded microgrids

This paper presents a unified probabilistic assessment of wind reserves for islanded microgrids. A multivariate nonparametric kernel density estimation algorithm is used to generate the probabilistic models of the wind resource, electrical demand and predicted performance of wind generation. These models are numerically combined to evaluate the capability of wind generation to act as a dynamic reserve and predict its performance for demand response, secondary generation and frequency regulation in an islanded system. The probabilistic model is robust against forecast errors and outlier events. Additionally, it captures multivariate cross-correlation, nonstationary environmental and load behavior, as well as multimodality in their underlying probability distributions. A case study is conducted to validate the proposed model, which predicts wind generation effectiveness for varying load profiles, wind profiles and generation capacities. PLEXIM simulation software is used to implement a model microgrid to demonstrate the integration of wind generation and its regulatory capabilities. The proposed algorithm has applications in power system planning and operation, and it provides probabilistic data for use in energy management and optimization of microgrids.