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Xinghao Fang

Publications and source records attributed to Xinghao Fang.

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Quantifying and Improving the Accuracy of Electromagnetic Transient-Transient Stability Hybrid Simulation

The increasing penetration of inverter-based resources introduces new dynamic challenges to modern power grids, such as sub- and super-synchronous oscillations and other faster dynamics. These dynamics are typically fast in nature and are difficult to accurately model and analyze using standard transient stability (TS) methods, necessitating the need for electromagnetic transient (EMT) analysis. However, EMT simulations are notoriously slow for large-scale grids due to both equation formulations and computational limitations. To overcome this challenge, EMT-TS hybrid simulation is often used, since it offers a balanced trade-off between accuracy and speed, making it feasible to perform EMT analysis on large systems. One open question about EMT-TS hybrid simulation is the accuracy of the EMT-TS boundary or interface. This paper introduces an error index to quantify EMT-TS hybrid interface errors, identifies conditions where the hybrid simulation approach may become inaccurate, and suggests EMT region expansions to improve the simulation accuracy. Additionally, a three-sequence hybrid interface model is proposed to mitigate inaccuracies caused by unbalanced conditions.

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Cloud Deployment of Large-Scale Electromagnetic Transient Simulation -- Discovery and Experiences

Electromagnetic Transient (EMT) simulation starts to play a critical role in modern power system planning and operations due to large penetration of inverter based resources (IBRs). The EMT studies are computationally intensive due to very small simulation time step and complex modeling of the protection and control of IBRs. It has been challenging for the traditional on-premises computing infrastructure to meet the ever-increasing computing needs of large-scale EMT studies. This paper shares experience of ISO New England (ISO-NE) on a pilot deployment of EMT simulation in a public cloud using Amazon Web Services. The platform can successfully meet the large-scale EMT simulation computation needs in a cost-effective way while meeting cyber security and data privacy requirements.

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