Blockchain and Deep Learning Development of Smart Charging of Electric Vehicles to Meet the Demand Side Management

Shirley, C. P.; Sonia, S. V. Evangelin; Sathya, V.; Manikandan, N.; Vidhyalakshmi, M. K.; Reddy, Ch. Venkata Krishna

doi:10.1109/icscds56580.2023.10105037

Cited by 3 publications

(1 citation statement)

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“…Monte Carlo Method (An and Zhang, 2023) mainly using computer generated "random numbers", Its essence is probability methodology (Shirley et al, 2023), By simulating experiments with infinitely large samples, the frequency of events can be obtained, minimizing the error in the probability of event occurrence as much as possible, and approaching the probability of real events infinitely.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Research on new energy vehicle charging prediction based on Monte Carlo algorithm and its impact on distribution network

Li,

Zhang

et al. 2023

Front. Energy Res.

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With the vigorous promotion of new energy policies, the large-scale charging of new energy vehicles has put forward higher requirements for the safety and stability of the distribution network. Based on the daily driving habits and charging patterns of new energy vehicles, a Monte Carlo sampling algorithm was used to establish a charging load model for new energy vehicles. The model analyzed the driving range, charging load, and time related parameters of new energy vehicles. By analyzing the law of daily charging power of new energy vehicles, the overall trend of charging load of new energy vehicles is obtained. Combined with the daily electricity consumption law of the distribution network, the total load of the distribution network is obtained, and the degree of impact on the distribution network is analyzed. This provides direction for the scheduling of future electric vehicle charging behavior and the construction of related supporting facilities, and provides strong guidance for the optimization and upgrading of the distribution network.

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Section: Monte Carlo Simulationmentioning

confidence: 99%