Plug-and-Play Regional Models for Real-Time Electromagnetic Transient Simulations of Large-Scale Power Grids: A Case Study of New York State Power Grid

Modeling large-scale electric power systems for electromagnetic transient (EMT) studies in real-time simulations is challenging due to factors such as real-time constraints, computational workload, extensive modeling efforts, and model accuracy. Developing a detailed and accurate EMT model of large-scale modern transmission networks is time-consuming and requires advanced computational capabilities. This paper proposes a Plug-and-Play (PnP) regional modeling approach to address the abovementioned challenges. An entire power grid is split into multiple regions, and each region is modeled with the PnP feature. A multi-regional model can be easily formed by merging multiple regional models due to the PnP capability. The advantage of PnP regional models is that it enhances the computational efficiency of an EMT simulation. Regional EMT models enable the accurate representation of a portion of the power system, allowing for accelerated simulation of local EMT models while providing a Thevenin equivalent of the rest of the system. A comprehensive procedure to develop and maintain the PnP regional models will be presented in this paper. A case study of the New York State (NYS) power grid is taken into consideration to demonstrate how utilities can utilize the proposed approach to develop their power grid models for EMT studies. A detailed explanation of using the PnP feature to form a multi-regional model is presented to provide a solid perspective on the application of developed PnP regional models. Furthermore, a code-based automated process is developed to maintain the real-time models up-to-date, which involves automatically processing input models and generating a real-time model accommodating all the changes. The significance of this study is to provide a guideline for handling large-scale power system modeling and deliver insight into the industry to the researcher.