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Accurate modeling of electric power transmission networks (EPTNs) is essential for real-time monitoring, operational awareness, and contingency analysis in power systems. Representing EPTNs as graphs with nodes and edges offers a powerful abstraction of the network topology. However, inferring this topology using only phasor measurement unit (PMU) data remains a challenge, especially with no prior network information. In this study, a quantum-classical hybrid approach based on the quantum approximate optimization algorithm (QAOA) to infer a transmission network graph model (TNGM) directly from PMU data is presented. The proposed approach utilizes a cost function incorporating the difference between power mismatch and mean power loss to guide one-to-one branch matching. Furthermore, the effect of quantum circuit depth is investigated to achieve 100% accuracy in TNGM construction. Typical results are presented on the two-area four-machine power system. 

The integration of electric vehicles (EVs) into the electric power distribution system poses numerous challenges and opportunities for optimizing energy management and system operations. Electric vehicle grid interfaces (EVGIs), essentially bidirectional power converters, allow for charging/grid-to-vehicle (G2V) and discharging/vehicle-to-grid (V2G) power transfers. A power dispatch estimation (PDE) model for V2G, based on availability of EVs in a distribution system and capabilities of the distribution system, is needed to assist in grid operations. This paper presents the development of a PDE model based on nodal power flows to capture the complex spatiotemporal dependencies inherent in G2V and V2G patterns. The hierarchical structure of a distribution system, feeder to EVGI node, is taken into consideration for PDE. Typical PDE estimation results are presented for the IEEE 34 test node feeder distribution system allocated with EVGIs.