A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile

Zhao, Zhonghao; Lee, Carman K.M.; Ren, Jingzheng

doi:10.1016/j.apenergy.2023.122278

Cited by 27 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of that paper was to create an algorithm that intelligently controls charging power based on the current state of charge of the vehicle. There are also numerous similar studies, such as [12][13][14][15][16], which also investigate the impact of certain parameters on other selected parameters of electric vehicles, design charging networks, or specify their characteristics.…”

Section: Introductionmentioning

confidence: 99%

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

Mądziel

2024

Energies

View full text Add to dashboard Cite

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

Mądziel

2024

Energies

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the escalating ownership of EVs has led to a concomitant rise in energy supply demands, posing significant challenges to the stability of power grid operations. The proliferation of EV charging loads has led to issues such as heightened peak load demand, regional charging bottlenecks, and potential grid overload [4]. The establishment of more precise spatiotemporal distribution models for EV charging loads can facilitate research into the optimization of the deployment and management of EV charging stations [5,6], the operation and control of power distribution networks [7], the utilization and regulation of EV transportation distribution coupled networks [8], and the optimized scheduling of charging and discharging processes [9][10][11], thereby promoting the sustainability of energy resources.…”

Section: Introductionmentioning

confidence: 99%

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

Bian,

Ren,

Guo

et al. 2024

Energies

View full text Add to dashboard Cite

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.

show abstract

Sustainable energy integration and optimization in microgrids: enhancing efficiency with electric vehicle charging solutions

Güven,

Yücel

2024

Electr Eng

View full text Add to dashboard Cite

A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile

Cited by 27 publications

References 44 publications

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

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

Sustainable energy integration and optimization in microgrids: enhancing efficiency with electric vehicle charging solutions

Contact Info

Product

Resources

About