Low Carbon Electric Vehicle Charging Coordination in Coupled Transportation and Power Networks

Yuan, Quan; Ye, Yujian; Tang, Yi; Liu, Xuefei; Tian, Qidong

doi:10.1109/tia.2022.3230014

Cited by 24 publications

(7 citation statements)

References 33 publications

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“…The integration of advanced technologies, including big data, 5G communication, cloud computing and vehicular internet, has facilitated instantaneous information exchange between users and interconnected networks [38]. On one hand, the real-time operational status of interconnected networks can serve as a fundamental basis for establishing dynamic electricity pricing.…”

Section: Predictive Framework For Spatiotemporal Distribution Of Ev C...mentioning

confidence: 99%

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Bian,

Ren,

Guo

et al. 2024

Energies

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A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

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Section: Predictive Framework For Spatiotemporal Distribution Of Ev C...mentioning

confidence: 99%

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Bian,

Ren,

Guo

et al. 2024

Energies

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show abstract

“…In recent years, electric vehicles have emerged as a new solution for energy industry restructuring and the development of electrified transportation [1]. However, with the sharp increase in electric vehicle users, the random driving behavior of electric vehicle users and their collective charging behavior will inevitably affect both the power grid and the transportation network [2]. The interaction between electric vehicles and the transportation and power systems will pose new challenges and opportunities [3][4].…”

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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“…In the current strategic environment of promoting the development of a low-carbon economy, electric vehicles (EVs) have also received significant support in the development of the automotive industry due to their energy-saving and emission reduction characteristics [1][2]. Charging facilities are the hub for achieving electric vehicle energy supply and interaction [3]. The random charging and load characteristics of large-scale electric vehicles will inevitably have an impact on the stable operation of the power grid.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling Strategy for Photovoltaic Charging Stations Participating in the Electricity Market based on Transactive Energy Mechanism

Wang,

Zhang,

Song

et al. 2023

E3S Web Conf.

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Regarding the optimization and management of energy in photovoltaic charging stations, existing research mostly focuses on the power grid, with little consideration given to its characteristics as a producer and consumer. This paper proposes an optimized scheduling strategy for photovoltaic charging stations that considers transactive energy mechanisms. Firstly, under the premise of ensuring the safe operation constraints of the distribution network, a distributed transaction model for photovoltaic charging stations was established. Secondly, the optimization model is solved using the Lagrangian decomposition principle and Subgradient method to obtain the scheduling plan of the photovoltaic charging station. The simulation results indicate that the scheduling strategy proposed in this article can promote the on-site consumption of renewable energy and improve the economy of photovoltaic charging stations.

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Low Carbon Electric Vehicle Charging Coordination in Coupled Transportation and Power Networks

Cited by 24 publications

References 33 publications

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

EV Charging Load Forecasting Based on Model Driven and Data Driven

Optimal Scheduling Strategy for Photovoltaic Charging Stations Participating in the Electricity Market based on Transactive Energy Mechanism

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