Aiming at the problem that the disorderly charging behavior of electric vehicles (EVs) connected to the grid affects the stability of the grid
a spatio-temporal prediction model of EV charging and swapping loads based on multi-layer complex travel chains is proposed under the condition of considering the accommodation of new energy. The model aims to reveal the dynamic compensation mechanism of swapping loads for the fluctuations of charging loads under the “charging-swapping” interaction mode. Firstly
the travel characteristics of EVs are determined under the multi-layer complex travel chain model
and the Floyd algorithm is used to find the optimal travel path. The unit mileage power consumption is determined by combining the “flow-speed” relationship. Secondly
the charging models of charging and swapping are respectively constructed
and the charging mode selected by EVs is determined under the “charging-swapping” interaction mode. Thirdly
a mathematical model of wind and solar power generation is established. Finally
the spatio-temporal distribution maps of the charging loads and new energy generation are formed through data processing
and the load data under different modes are compared. The experimental results show that the charging demand generated by EVs during driving has obvious regional differences
and the swapping load can effectively mitigate the fluctuations of charging loads under the accommodation of new energy.
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references
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Unterluggauer T, Rauma K, Järventausta P, et al.Short-term load forecasting at electric vehicle charging sites using a multivariate multi-step long short-term memory: a case study from Finland[J]. IET Electrical Systems in Transportation, 2021, 11(4): 405-419.
Zhang X, Chan K W, Li H R, et al.Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model[J]. IEEE Transactions on Cybernetics, 2021, 51(6): 3157-3170.