Optimal Planning of Electric Vehicle Battery Centralized Charging Station Based on EV Load Forecasting

He, Chenke; Zhu, Jizhong; Lan, Jing; Li, Shenglin; Wu, Wanli; Zhu, Haohao

doi:10.1109/tia.2022.3186870

Cited by 30 publications

(6 citation statements)

References 41 publications

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“…National household travel survey (NHTS) data is used to model the EVs charging demand [9,58,59]. The EV daily initial departure time is represented by the gamma distribution (1).…”

Section: The Temporal Characteristics Analysis Of Travelsmentioning

confidence: 99%

Coordinated planning of charging swapping stations and active distribution network based on EV spatial‐temporal load forecasting

Zhu

Borghetti

et al. 2023

IET Generation Trans & Dist

Self Cite

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Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study.

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“…National household travel survey (NHTS) data is used to model the EVs charging demand [9,58,59]. The EV daily initial departure time is represented by the gamma distribution (1).…”

Section: The Temporal Characteristics Analysis Of Travelsmentioning

confidence: 99%

Coordinated planning of charging swapping stations and active distribution network based on EV spatial‐temporal load forecasting

Zhu

Borghetti

et al. 2023

IET Generation Trans & Dist

Self Cite

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“…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

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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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“…Based on the road topology of the planning area, a road topology model, as well as models for electric vehicle travel energy consumption and range, are constructed. Taking into consideration the photovoltaic (PV) distribution system, a BSS planning model is developed with the objective of minimizing the annual comprehensive cost [12]. This study proposes a two-stage optimization framework for the charging infrastructure planning and charging scheduling of battery electric bus systems.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Layer MPC of Coordinated Control of Battery Load Demand and Grid-Side Supply Matching at Electric Vehicle Swapping Stations

Tang,

Zhang,

Zhang

et al. 2024

Energies

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The uncontrolled charging of electric vehicles may cause damage to the electrical system as the number of electric vehicles continues to rise. This paper aims to construct a new model of the power system and investigates the rational regulation and efficient control of electric vehicle battery charging at electric vehicle exchange battery stations in response to the real-time grid-side supply situation. Firstly, a multi-objective optimization strategy is established to meet the day-ahead forecasted swap demand and grid-side supply with the maximization of day-ahead electric vehicle battery swapping station (BSS) revenue in the core. Secondly, considering the variable tariff strategy, a two-layer Model Predictive Control (MPC) coordinated control system under real-time conditions is constructed with the objective function of maximizing the revenue of BSS and smoothing the load fluctuation of the power system. Then, the day-ahead optimization results are adopted as the reference value for in-day rolling optimization, and the reference value for in-day optimization is dynamically adjusted according to the real-time number of electric car changes and power system demand. Finally, verified by experimental simulation, the results show that the day-ahead-intraday optimization model can increase the economic benefits of BSS and reduce the pressure on the grid to a certain extent, and it can ensure the fast, accurate, and reasonable allocation of batteries in BSS, and realize the flexible, efficient, and reasonable distribution of batteries in BSS.

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Optimal Planning of Electric Vehicle Battery Centralized Charging Station Based on EV Load Forecasting

Cited by 30 publications

References 41 publications

Coordinated planning of charging swapping stations and active distribution network based on EV spatial‐temporal load forecasting

Coordinated planning of charging swapping stations and active distribution network based on EV spatial‐temporal load forecasting

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

A Dual-Layer MPC of Coordinated Control of Battery Load Demand and Grid-Side Supply Matching at Electric Vehicle Swapping Stations

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