A Schedule-Control Aided Strategy for Charging Large Number of EVs Under Normal and Line Failure Scenarios

Mao, Tian; Lau, Wing Cheong; Shum, Chong; Chung, H.S.H.; Tsang, Kim-Fung; Tse, Norman C. F.

doi:10.1109/access.2017.2712130

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…The scale of charging and switching power stations connected to the power grid is increasing, and it also has a huge impact on power system. Charging and discharging behavior of electric vehicles will not only increase the operation status of generating units but also increase the instability of power grid operation, which will bring pressure to the operation of transmission network 1,2 . Therefore, it is necessary to guide the energy exchange between the charging and switching power stations of electric vehicles and the power grid, to take advantage of the energy storage characteristics of the charging and switching power stations to participate in the load management of the power grid, to optimize the time period of the charging and switching power stations of electric vehicles, and to realize the orderly charging of the charging and switching power stations.…”

Section: Introductionmentioning

confidence: 99%

An electric vehicle operation optimization method based on demand‐side management

Gan

Chui-yong

2019

Concurrency and Computation

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Against the background of energy restriction and environmental pollution, in recent years, new energy electric vehicles have developed rapidly with their advantages of energy saving and environmental protection. To realize the benign interaction between electric vehicle charging and switching power stations and power grid, and the demand-side management of electric vehicle charging and switching power stations, the Monte Carlo random sampling method is used to collect the battery-charged state of batteries in one day. Considering the constraints of charging and discharging times of charging stations, the operation model of charging and switching stations for electric vehicles is established, and the interior point optimization method is used to solve the model; thus, the optimal orderly charging and discharging strategy for electric vehicles can be obtained. Finally, simulation results show that the optimized charging mode can effectively reduce the peak-valley difference of the system load and improve the stability of the system compared with the stochastic charging mode. In addition, this mode of charging can effectively develop the economic potential of the electric-vehicle batteries without affecting the use needs of the owners, and it has good prospects for engineering popularization.

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Section: Introductionmentioning

confidence: 99%

An electric vehicle operation optimization method based on demand‐side management

Gan

Chui-yong

2019

Concurrency and Computation

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“…Prior research related to the influence of EV penetration into the power system includes analysis of the voltage sensitivity of the distribution system according to EV charging location [2], analysis of the effects on the power system [3][4][5], and study of the stability of the system using EVs [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Study on EV Charging Peak Reduction with V2G Utilizing Idle Charging Stations: The Jeju Island Case

Han

Son

2018

Energies

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Electric vehicles (EVs), one of the biggest innovations in the automobile industry, are considered as a demand source as well as a supply source for power grids. Studies have been conducted on the effect of EV charging and utilization of EVs to control grid peak or to solve the intermittency problem of renewable generators. However, most of these studies focus on only one aspect of EVs. In this work, we demonstrate that the increased demand resulting from EV charging can be alleviated by utilizing idle EV charging stations as a vehicle-to-grid (V2G) service. The work is performed based on data from Jeju Island, Korea. The EV demand pattern in 2030 is modeled and forecasted using EV charging patterns from historical data and the EV and charging station deployment plan of Jeju Island's local government. Then, using a Monte Carlo simulation, charging and V2G scenarios are generated, and the effect of V2G on peak time is analyzed. In addition, a sensitivity analysis is performed for EV and charging station deployment. The results show that the EV charging demand increase can be resolved within the EV ecosystem.

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A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior

Yaghoubi,

Khamees

et al. 2024

Engineering Applications of Artificial Intelligence

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A Schedule-Control Aided Strategy for Charging Large Number of EVs Under Normal and Line Failure Scenarios

Cited by 5 publications

References 35 publications

An electric vehicle operation optimization method based on demand‐side management

An electric vehicle operation optimization method based on demand‐side management

Study on EV Charging Peak Reduction with V2G Utilizing Idle Charging Stations: The Jeju Island Case

A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior

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