Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification

Zhuang, Zhiyuan; Zheng, Xuemei; Chen, Zixing; Jin, Tao; Li, Zengqin

doi:10.3390/en15197021

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…For example, in literature [5], an electric vehicle charging demand prediction model was proposed, considering road topology characteristics, but it did not account for the influence of environmental temperature and speed factors on electric vehicle energy consumption. In literature [6], an electric vehicle charging demand prediction model was proposed, which considered environmental temperature and vehicle speed. It used the origin-destination matrix method to obtain the start and end points of private car and taxi trips.…”

Section: Introductionmentioning

confidence: 99%

EV Charging Load Forecasting Based on Model Driven and Data Driven

Tai,

Chen,

Wang

et al. 2024

Preprint

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Based on the integration of dynamic traffic information, environmental temperature, real-time traffic flow, queuing theory, and other methods, a novel deep learning architecture for predicting Origin-Destination traffic flow in urban transportation systems has been developed to forecast the spatial and temporal distribution of electric vehicle charging loads.This method begins by analyzing the impact of various factors, such as urban traffic network data, daily patterns, and weather, on the driving patterns of electric vehicles. It employs a Graph Convolutional Recursive Neural Network algorithm to separately identify the starting and ending points of private cars and taxis.Next, it introduces impedance models for road segments and nodes, which take into account dynamic traffic information, intersection flow, and an air conditioning energy consumption model that considers environmental temperature and real-time vehicle speed. The method utilizes Graph Convolutional Network to extract spatial features of traffic nodes and their neighboring nodes. Time-related features are extracted using P-Prophet, creating a traffic intersection traffic flow prediction model.To optimize the minimum cost travel routes for electric vehicles, it improves the Floyd dynamic algorithm using a sparse graph optimization strategy, thereby simulating the driving behavior of electric vehicle users. Additionally, it uses K-means clustering to analyze potential charging preferences of electric vehicle users, providing insights into characteristic charging behaviors among typical urban electric vehicle users.

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Section: Introductionmentioning

confidence: 99%

EV Charging Load Forecasting Based on Model Driven and Data Driven

Tai,

Chen,

Wang

et al. 2024

Preprint

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“…Forecasting EV charging load demand is a prerequisite for proper charging station planning. Considering the effects of high uncertainty in weather, traffic, and driver behavior, Wu et al [12] proposed an optimal parameter prediction method that can improve the prediction accuracy of charging demand of EVs in MG. Zhuang et al [13] combined a multi-source information architecture consisting of an information layer, an algorithm layer, and a model layer to predict the loads of the EV control system in the region. They also proposed a decision-making method for EV charging stations based on prospect theory.…”

Section: Introductionmentioning

confidence: 99%

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

Gan,

Ruan,

Wang

et al. 2024

Energies

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With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.

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“…The objective was to capture the uncertainties in EV charging forecasting by adopting Bayesian theory and neural networks. In [21], a forecasting method based on multi-source data and prospect theory was presented. The travel behaviour of private electric vehicles and taxi owners was considered along with the roads' velocity and network.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach

et al. 2023

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Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.

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Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification

Cited by 12 publications

References 21 publications

EV Charging Load Forecasting Based on Model Driven and Data Driven

EV Charging Load Forecasting Based on Model Driven and Data Driven

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach

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