An Electric Vehicle Charging Load Prediction Method Based on Travel Trajectory Characteristics

Xiao, Qian; Wang, Wei; Zheng, Le; Si, Shuang; Chen, Jianjian; Wang, Ning

doi:10.1109/ei256261.2022.10116549

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…However, since it takes some time to find a charging station and charge, it is necessary to analyze the subjective wishes of EV users and the time it takes to charge when judging whether an EV is charging. Some articles assume that after the SOC of EVs drops to a certain value, EV users will look for charging stations [21,22]. However, these settings ignore the differences between different EV users and the impact of SOC reduction on users' willingness to charge.…”

Section: Soc-based Acceptance Threshold Of Charging Timementioning

confidence: 99%

Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

Shi,

Liu,

Wang

et al. 2024

Preprint

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The access of electric vehicle charging stations (EVCS) brings challenges to the stable operation of the distribution network.At present, there is a lack of indicator to quantify the economic losses caused by the decrease in power quality of the distribution network due to the access of EVCSs.In the paper, the travel trajectories of electric vehicle users are constructed through trip chain and state transition matrices,thereby obtaining the spatiotemporal distribution of charging loads.In addition, the voltage deviation and line loss caused by charging loads are unified into economic indicator to quantify.The simulations are conducted in a road network coupled to the IEEE 33-node distribution network.The result shows that the charging load of electric vehicle charging stations have a significant impact on the power quality of the distribution network.At the same time, optimizing the location of charging stations and guiding electric vehicle users’ charging behaviorcan effectively improve the power quality and economic efficiency of distribution networks.

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Section: Soc-based Acceptance Threshold Of Charging Timementioning

confidence: 99%

Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

Shi,

Liu,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It analyzes the charging load of EVs, without considering their driving habits and charging randomness. Reference (Qian et al, 2022) A charging load prediction model for EVs was constructed, based on the characteristics of vehicle driving behavior. The region of the vehicle was determined through a function, and the position of EVs was described using Weibull probability distribution to form the vehicle's charging network, and constructing charging load prediction models for private electric vehicles and electric taxis.…”

Section: Introductionmentioning

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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An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

Huaman-Rivera,

Calloquispe-Huallpa,

Luna Hernandez

et al. 2024

Electronics

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The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.

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An Electric Vehicle Charging Load Prediction Method Based on Travel Trajectory Characteristics

Cited by 3 publications

References 10 publications

Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

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

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

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