Evaluating the impact of Plug-in Hybrid Electric Vehicles on composite power system reliability

Green, Robert C.; Wang, Lingfeng; Alam, M. S.; Depuru, Soma Shekara Sreenadh Reddy

doi:10.1109/naps.2011.6024870

Cited by 28 publications

(9 citation statements)

References 10 publications

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“…Different models and settings have been considered so far to conduct this kind of studies. In [12], for example, a probabilistic method of examining the impact of PEVs on composite power systems is presented. Results suggest that the PEVs penetration will indeed affect the system dynamic response.…”

Section: B Literature Reviewmentioning

confidence: 99%

Decentralized Charging of Plug-In Electric Vehicles and Impact on Transmission System Dynamics

Moschella

Murad

Crisostomi

et al. 2021

IEEE Trans. Smart Grid

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This paper focuses on the impact of the charge of Plug-in Electric Vehicles (PEVs) on the dynamic response of power systems and proposes an efficient solution to control electric vehicle chargers, by dynamically allocating the available power in an optimized way. The proposed approach is based on an Additive-Increase-Multiplicative-Decrease (AIMD) stochastic decentralized control strategy to efficiently and seamlessly manage the charge of a high number of PEVs with little communication efforts. A modified version of the New England network is utilized to validate the proposed control through a variety of scenarios and control setups. Index Terms-Plug-in electric vehicles, loading margin, frequency control, decentralized control.

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Section: B Literature Reviewmentioning

confidence: 99%

Decentralized Charging of Plug-In Electric Vehicles and Impact on Transmission System Dynamics

Moschella

Murad

Crisostomi

et al. 2021

IEEE Trans. Smart Grid

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show abstract

“…By using (14)–(16), this model can consider different types and different penetration of EVs, ranging from 0 to 100% relative to conventional vehicles

N_{t} = normal ()(, μ_{c}, σ_{c}), normal∀ c

μ_{c} = N_{ev, c} \times P_{ev, c} normal∀ c

σ_{c} = 0.01 \times μ_{c}

here

P_{ev, c}

is the probability of different classes of EV.

N_{ev}

is the total number of EVs and is assumed to be by a normal distribution having mean

μ_{c}

and standard deviation

σ_{c}

[29]. In this work, we assume that all vehicles are electric and belong to two class, so,

N_{tot} = N_{ev}

.…”

Section: Stochastic Modelling Of Ev Storage Availabilitymentioning

confidence: 99%

Stochastic modelling of electric vehicle behaviour to estimate available energy storage in parking lots

2020

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“…Elektrikli araç yüklerinin etkileri değerlendirilirken elektrikli araçların geleneksel taşıtlar gibi her an park edilebilir karakteristikleri ve şarja başlama anındaki şarj durumlarının (SOC) tamamen rastlantısal olduğu genellikle gözden kaçmaktadır [11]. Elektrikli araçların şarj edilme olasılığı ile şarj-deşarj süreçleri ve farklı kontrol teknikleri ile zamana bağlı değerlendirmeler [14][15][16]'da Monte Carlo metodu ile karşılaştırmalı olarak değerlendirilmiş ve güvenilirlik yönünden incelenmiştir.…”

Section: Gi̇ri̇ş (Indtroduction)unclassified

Elektrikli araç şarj istasyonu yüklerinin güvenilirlik indisleri üzerine etkilerinin incelenmesi

Güneş¹,

Tekdemir²,

Şengül³

et al. 2018

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Analysing the impact of charge station load on reliability indices  Probabilistic modelling  Analysis on real distribution network model Figure A. Estimated changes in SAIDI and SAIFI indices for the investigated years Purpose: Rapid increase in the number of electric vehicles and charging station loads challenges the adequacy and reliability of distribution networks. In order to plan and operate network properly, operators should have future projections includes electric vehicle charging station loads. Theory and Methods: Probabilistic modelling approach used in this study includes usage of the Probability Density Function (PDF) of Weibull distribution, Latin Hypercube Sampling (LHS) method, correlation between the loads and generation of optimum scenarios by applying reordering algorithm (ROA) in the context of suitable correlation between loads. Results: As a result of this study, it is seen that electric vehicle technology, which is expected to replace traditional fossil fuel-based vehicles, will become a major burden on distribution networks in particular in the near future, and may seriously increase operational risks. In this study, the effects of the electric vehicles and charging station loads on the reliability indices in the distribution network were assessed with probabilistic approach for upcoming years considering the loads of the electric vehicles and the charging station would increase rapidly in the world. Reliability. Conclusion: With the increase in the number of electric vehicles, it is seen that the calculated SAIDI and SAIFI values have increased and that the index value increased about 2.5 times in 20 years forward vision. This indicates that the planning work and future projections should not ignore electric vehicle loads.

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Evaluating the impact of Plug-in Hybrid Electric Vehicles on composite power system reliability

Cited by 28 publications

References 10 publications

Decentralized Charging of Plug-In Electric Vehicles and Impact on Transmission System Dynamics

Decentralized Charging of Plug-In Electric Vehicles and Impact on Transmission System Dynamics

Stochastic modelling of electric vehicle behaviour to estimate available energy storage in parking lots

Elektrikli araç şarj istasyonu yüklerinin güvenilirlik indisleri üzerine etkilerinin incelenmesi

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