Abstract

During the process of vehicle–grid interaction, the charging load of electric vehicles shows significant uncertainty, which is driven by multiple user behavior variables: including the differentiated characteristics of users’ daily travel needs, as well as personalized charging habits, random charging periods, and dynamic changes in charging power demands. To address the scheduling challenges arising from the uncertainty of electric vehicle loads in the interaction between electric vehicles and the power grid, this paper proposes a multi-objective optimization scheduling method for the interaction between electric vehicles and the power grid, which takes into account the uncertainty of power sources and loads. This method can enhance the economic operation level of the power grid, increase the acceptance capacity of renewable energy, and improve the stability of the system. Firstly, this paper proposes an improved K-means clustering algorithm, which combines Monte Carlo sampling to achieve the generation and reduction of scenarios for electric vehicle load and photovoltaic output. Secondly, a scheduling framework based on the vehicle–grid collaborative interaction mode is constructed, and a random optimization scheduling model for photovoltaic storage electric vehicles is established. Finally, an example of a photovoltaic storage charging station in an industrial park is used for verification. The simulation results demonstrate the economic feasibility and effectiveness of this scheduling strategy.

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