Hierarchical and distributed optimization of distribution network considering spatial and temporal distribution of electric vehicle charging load

Liu, Shu; Chen, Fang; Jia, Shicheng; Xiang, Yueping; Yang, Jun

doi:10.1016/j.egyr.2023.04.086

Cited by 1 publication

(3 citation statements)

References 20 publications

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“…The deceleration rule represents the psychological activity of users avoiding risk, as delineated in (7).…”

Section: Deceleration Behavior Updatementioning

confidence: 99%

“…Initialize the routing matrix Z; initialize the optimal time matrix T, as presented in (10). The deceleration rule represents the psychological activity of users avoiding risk, as delineated in (7).…”

Section: Step2mentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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“…The deceleration rule represents the psychological activity of users avoiding risk, as delineated in (7).…”

Section: Deceleration Behavior Updatementioning

confidence: 99%

Section: Step2mentioning

confidence: 99%