Coordinated charging strategies for plug-in electric vehicles to ensure a robust charging process

Hüls, Jannik; Remke, Anne

doi:10.4108/eai.25-10-2016.2266997

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…We first consider a model of an electric vehicle charging station. It is a Modest model adapted from the "extended" case study of [42]: a stochastic hybrid Petri net with general transitions, which in turn is based on the work in [31]. The scenario we model is of an electric vehicle being connected to the charger every evening in order to be recharged the next morning.…”

Section: Methodsmentioning

confidence: 99%

A Statistical Model Checker for Nondeterminism and Rare Events

Budde

D’Argenio

Hartmanns

et al. 2018

Tools and Algorithms for the Construction and Analysis of Systems

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Statistical model checking avoids the state space explosion problem in verification and naturally supports complex non-Markovian formalisms. Yet as a simulation-based approach, its runtime becomes excessive in the presence of rare events, and it cannot soundly analyse nondeterministic models. In this tool paper, we present modes: a statistical model checker that combines fully automated importance splitting to efficiently estimate the probabilities of rare events with smart lightweight scheduler sampling to approximate optimal schedulers in nondeterministic models. As part of the Modest Toolset, it supports a variety of input formalisms natively and via the Jani exchange format. A modular software architecture allows its various features to be flexibly combined. We highlight its capabilities with an experimental evaluation across multi-core and distributed setups on three exemplary case studies.

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Section: Methodsmentioning

confidence: 99%

A Statistical Model Checker for Nondeterminism and Rare Events

Budde

D’Argenio

Hartmanns

et al. 2018

Tools and Algorithms for the Construction and Analysis of Systems

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show abstract

“…We first consider a model of an electric vehicle charging station. It is a Modest model adapted from the "extended" case study of [67]: a stochastic hybrid Petri net with general transitions, which in turn is based on the work in [50]. The scenario we model is of an electric vehicle being connected to the charger every evening in order to be charged the next morning.…”

Section: Electric Vehicle Chargingmentioning

confidence: 99%

An efficient statistical model checker for nondeterminism and rare events

Budde

D’Argenio

Hartmanns

et al. 2020

Int J Softw Tools Technol Transfer

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Statistical model checking avoids the state space explosion problem in verification and naturally supports complex non-Markovian formalisms. Yet as a simulation-based approach, its runtime becomes excessive in the presence of rare events, and it cannot soundly analyse nondeterministic models. In this article, we present : a statistical model checker that combines fully automated importance splitting to estimate the probabilities of rare events with smart lightweight scheduler sampling to approximate optimal schedulers in nondeterministic models. As part of the Modest Toolset, it supports a variety of input formalisms natively and via the Jani exchange format. A modular software architecture allows its various features to be flexibly combined. We highlight its capabilities using experiments across multi-core and distributed setups on three case studies and report on an extensive performance comparison with three current statistical model checkers.

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“…The degradation of voltage profile, increase in peak load, harmonic distortions are some of the consequences of the uncoordinated charging of EVs. Many references demonstrate the adverse impact of EV charging loads on different parameters of the distribution network like voltage profile [8][9][10][11][12][13][14], harmonics [15][16][17][18] and peak load [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…In [23] the authors analysed the impact of fast EV chargers on a retail building's load demand and concluded that 38% of the PHEV load demand could be absorbed by demand management and photovoltaics. In [24] the authors proposed a two stage demand response model to control the increase in peak load due to the charging of EVs.…”

Section: Introductionmentioning

confidence: 99%

“…The different detrimental impacts of EV charging station loads like voltage instability, harmonic distortion, and power losses on distribution network are analysed in [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. However, there is a dearth of literature focusing on the impact of the EV charging station load on all the aforementioned parameters considered together.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Electric Vehicle Charging Station Load on Distribution Network

et al. 2018

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Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP) index. The results obtained indicate the efficacy of the VRP index.

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Coordinated charging strategies for plug-in electric vehicles to ensure a robust charging process

Cited by 11 publications

References 10 publications

A Statistical Model Checker for Nondeterminism and Rare Events

A Statistical Model Checker for Nondeterminism and Rare Events

An efficient statistical model checker for nondeterminism and rare events

Impact of Electric Vehicle Charging Station Load on Distribution Network

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