Charging load from large-scale plug-in hybrid electric vehicles: Impact and optimization

Yao, Yin; Gao, David Wenzhong

doi:10.1109/isgt.2013.6497887

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…The stochastic behavior of PHEVs is an outcome of numerous factors, such as battery capacity, the number of vehicles, charging speeds, time at which it is plugged in or plugged out, daily distance traveled by an EV, type of vehicle, and charging patterns. 43 In this study, the load modeling of PHEVs is based on some factors, such as daily distance traveled by an EV, the number of vehicles, time at which it is plugged in, that is, arrival time and plugged out, that is, departure time. The parameters such as daily distance traveled by an EV, arrival time, and departure time are extracted by using Monte Carlo simulation from their respective probability density functions and information regarding these probability density functions are reported in Ref.…”

Section: Phevs Load Modelsmentioning

confidence: 99%

Optimal energy management in microgrid including stationary and mobile storages based on minimum power loss and voltage deviation

Sharma

Mishra

Mathur

2021

Int Trans Electr Energ Syst

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The grid-connected microgrid (MG) may depend on the utility grid (UG) during peak load hours to meet its unequaled load demand. Also, the rapid increase in penetration of plug-in hybrid electric vehicles (PHEV) affects the system's stability and the load demand. Therefore, it is an indisputable fact that MG demands, an effective energy management strategy to manage the peak load that occurred due to PHEV charging. To address this challenge, an optimal energy management strategy (OEMS) is proposed that aims at maximum utilization of DERs, promotes energy trading, and diminishes the depen-

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Section: Phevs Load Modelsmentioning

confidence: 99%