Online Long-Term Voltage Stability Margin Estimation for IBR/DER Dominated Power System with Integrated VSM-Aware TSO-DSO Framework
Rattachement africain : us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
The rapid growth of inverter-based resources (IBRs) and distributed energy resources (DERs) has fundamentally altered the long-term voltage stability characteristics of modern power systems. This article leverages the advantages of machine learning (ML) for the online estimation of long-term voltage stability margin (VSM) and enhancement of VSM through coordinated transmission system operator-distribution system operator (TSO-DSO) optimization. An explicit analytical VSM expression is derived from offline T&D co-simulation data using a physics-informed ML-trained model under probabilistic loading and generation mix scenarios, while accounting for unbalanced distribution modeling. The resulting closed-form VSM representation is linearized and embedded into the TSO optimization problem, enabling real-time enforcement of minimum VSM constraints. We further enhance operational efficiency by incorporating VSM sensitivities into both transmission and distribution optimization, allowing prioritization of the most influential reactive power resources. Simulation studies conducted on the IEEE 30-bus transmission network integrated with multiple IEEE 37-node distribution feeders validate that the proposed framework successfully achieves the desired VSM enhancement while maintaining high estimation accuracy.
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Iowa State University Department of Electrical and Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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Richland College pays non établi dans la noticeUniversité ou école supérieure
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Arizona State University pays non établi dans la noticeUniversité ou école supérieure
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School of Electrical pays non établi dans la noticeUniversité ou école supérieure
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Senior Researcher pays non établi dans la noticeInstitution
Department of Electrical and Computer Engineering — Iowa State University, Richland College et Arizona State University, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.