Large-scale electric vehicle integration poses significant challenges to power grid operation, demanding high-fidelity and diversified datasets for in-depth research. To address this need, we introduce MP-EVData, a comprehensive dataset of station-level charging load profiles from a major Chinese metropolis in 2024. The core value of MP-EVData is providing charging load data for 10 stations in the same geographical location during the same time period, representing five distinct prototypes: taxi demonstration stations, bus depots, residential charging stations, battery swapping stations and heavy-duty truck stations. This unique structure eliminates the disturbances of external variables such as geographical, climatic, and policy, enabling controlled comparative analysis of their load characteristics. Furthermore, the dataset is augmented with a parallel, high-fidelity synthetic dataset generated using advanced generative AI models to support data-intensive research. Technical validation reveals highly distinct daily, weekly, and annual temporal patterns across prototypes and demonstrates clear price-responsive charging behavior under time-of-use pricing. MP-EVData provides a crucial benchmark for advancing researches in load forecasting, smart charging algorithms and urban infrastructure planning.
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