Transmission Contingency Analysis Based on Data-Driven Equivalencing of Radial Distribution Networks Considering Uncertainties

• Main Authors: Kunjie Tang, Mingyang Ge, Shufeng Dong, Jianye Cui, Xiang Ma
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Data-driven equivalencing; offline-online-combined model training; power flow; transmission contingency analysis; uncertainties
• Online Access: https://ieeexplore.ieee.org/document/9298813/

A transmission contingency analysis (TCA) method based on data-driven equivalencing of radial DN distribution networks is proposed. First, an offline-online-combined data-driven model training method is proposed. The historical data are exploited during offline model training considering the uncertainties of loads and distributed generations to achieve partially prepared root nodal power injection functions, where root nodal voltage magnitudes are taken as arguments. After that, the real-time data of loads and DGs are used to determine all coefficients in these functions. In the proposed TCA, DNs will be equivalent to simplified models by distribution system operators (DSOs) with the data-driven method and the models will be sent to the transmission system operator (TSO). Then, TSO can complete the TCA independently. Numerical experiments show that the proposed TCA approach has similar accuracy and higher efficiency compared with the traditional global-power-flow-based TCA approach. It not only significantly reduces communication time between TSO and DSOs, but also saves calculation times, which may benefit real practice in the coordination operation of transmission-distribution-coupled systems in the future.