Simultaneous coordination of distinct plug-in Hybrid Electric Vehicle charging stations: A modified Particle Swarm Optimization approach

Suganya, S.; Raja, S. Charles; Venkatesh, P.

doi:10.1016/j.energy.2017.07.036

Cited by 45 publications

(17 citation statements)

References 22 publications

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“…In addition to PHEVs, the contribution of renewable in decreasing emission is inevitable. 5 An optimal framework for power transaction is proposed by Adnan et al 6 to maximize the DG penetration into a micro-grid. 2 Thus, adding source or load to an existing distribution system will cause changes to the flow of power in lines and loads resulting in change of power loss and voltage profile.…”

Section: Discussionmentioning

confidence: 99%

“…Such results also give some suggestions about voltage profile improvement based on environmental factors and services of CSs. 5 An optimal framework for power transaction is proposed by Adnan et al 6 to maximize the DG penetration into a micro-grid. Three different coordinated wind-PHEV energy dispatching algorithms in a vehicle to grid content are also discussed.…”

Section: Introductionmentioning

confidence: 99%

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Smart utilization of renewable energy sources in a microgrid system integrated with plug-in hybrid electric vehicles

et al. 2017

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Summary Mass roll‐out of plug‐in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi‐zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real‐time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm‐based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart utilization of renewable energy sources in a microgrid system integrated with plug-in hybrid electric vehicles

et al. 2017

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“…Particle swarm optimization (PSO) [154] Each agent has a velocity and position vector, which are updated according to the best so far positions of particle and population over the course of evolution.…”

Section: Imitate the Theory Of Darwinian Evolutionmentioning

confidence: 99%

Local flexibility markets: Literature review on concepts, models and clearing methods

2020

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“…In managing the distribution system with the addition of new units, it is essential that the location selected results in minimum increments in power loss [11]. CS is a heavy load that, when placed on a particular bus in the distribution network, results in increment in power loss [12]. The main goal is to select the optimal location in the distribution system at which the increment in power loss is as low as possible.…”

Section: Power Lossmentioning

confidence: 99%

Electric vehicles in a smart grid: a comprehensive survey on optimal location of charging station

Bilal

Rizwan

2020

IET Smart Grid

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The burning of fossil fuels and the emission of greenhouse gases motivates policymakers to think about the transition in their approach towards electric vehicles (EVs) from conventional ones. Transportation vehicles' electrification drives the attention of various researchers and scientists towards the emergence of charging stations (CSs). CS placement is a matter of great concern for large scale penetration of EVs. Old infrastructure causes several challenges in planning the ideal placement of the CS since EVs have not prevailed in recent years. Recently, a lot of studies have been performed on CS placement, which attracts the attention of researchers. Various approaches, objective functions, constraints and range of optimisation techniques are addressed by researchers for optimal placement of CS. This study provides the research outcomes in respect of the placement of CS over the past few years based on objective functions, solution methods, geographic conditions and demandside management.

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Simultaneous coordination of distinct plug-in Hybrid Electric Vehicle charging stations: A modified Particle Swarm Optimization approach

Cited by 45 publications

References 22 publications

Smart utilization of renewable energy sources in a microgrid system integrated with plug-in hybrid electric vehicles

Smart utilization of renewable energy sources in a microgrid system integrated with plug-in hybrid electric vehicles

Local flexibility markets: Literature review on concepts, models and clearing methods

Electric vehicles in a smart grid: a comprehensive survey on optimal location of charging station

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