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• W3208418608 abstract "Widespread utilization of electric vehicles (EVs) incurs more uncertainties and impacts on the scheduling of the power-transportation coupled network. This paper investigates optimal power scheduling for a power-transportation coupled network in the day-ahead energy market considering multiple uncertainties related to photovoltaic (PV) generation and the traffic demand of vehicles. Specifically, the EV drivers choose the lowest-cost routes in response to electricity prices and the power operators maximize their operating profits. Furthermore, we show the interactions between the power system and EV users from a potential game-theoretic perspective, and the scheduling problem of the equilibrium of power-transportation coupled network can be interpreted by a two-stage distributionally robust optimization (DRO) model. In addition, uncertainties of PV generation and traffic demand are established as scenario-based ambiguity sets combined with the historical distribution information, respectively. A combination of the duality theory and the Benders decomposition is developed to solve the DRO model. Simulation results demonstrate the effectiveness and applicability of the proposed approach." @default.
• W3208418608 created "2021-11-08" @default.
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• W3208418608 date "2021-10-27" @default.
• W3208418608 modified "2023-09-27" @default.
• W3208418608 title "Distributionally Robust Day-ahead Scheduling for Power-traffic Network Considering Multiple Uncertainties under a Potential Game Framework" @default.
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• W3208418608 hasPublicationYear "2021" @default.
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