Distribution transformer stress in smart grid with coordinated charging of Plug-In Electric Vehicles

Masoum, Mohammad A. S.; Moses, Paul; Hajforoosh, Somayeh

doi:10.1109/isgt.2012.6175685

Cited by 78 publications

(59 citation statements)

References 13 publications

(19 reference statements)

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“…The resulting impact on low voltage distribution systems can be significant, with potential problems including transformer or line overload [2], harmonics [3], voltage drop [4], and phase unbalance [5].…”

Section: Introductionmentioning

confidence: 99%

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

Hoog

Thomas

Müenzel

et al. 2013

2013 IEEE Power &Amp; Energy Society General Meeting

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Abstract-The expected rise of electric vehicles will lead to significant additional demand on low voltage (LV) distribution systems. Uncontrolled charging could lead to problems such as thermal overload of transformers and lines, voltage deviation, harmonics, and phase unbalance. We propose two electric vehicle charging algorithms, one centralized and one distributed, and compare their performance in simulations that use real vehicle data, on a model based on a real LV network in northern Melbourne, Australia. Our experiments confirm that the locations of the vehicles in the network are an important factor in predicting adverse effects. Furthermore, our coordinated charging solutions allow penetrations of electric vehicles approximately 3-6 times higher than is possible using uncoordinated charging, in our network.

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Section: Introductionmentioning

confidence: 99%

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

Hoog

Thomas

Müenzel

et al. 2013

2013 IEEE Power &Amp; Energy Society General Meeting

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show abstract

“…As a result of these, the following problems may arise and they are listed as overloads in cables and distribution transformers, elevated power losses and no guarantee for grid reliability and thus cause increase in overall system cost [5]. An experiment has been conducted in the 1,200 node test system in Western Australia [6] to examine the effects of random uncoordinated charging of EV on transformers.…”

Section: Introductionmentioning

confidence: 99%

“…A significant load surging and voltage deviations are observed from the test results even with low EV penetrations. A 14 % of increase of load from 17 % on transformers has significantly proven a rise in currents of the transformer with the penetrations of EV [5].…”

Section: Introductionmentioning

confidence: 99%

“…But, the coordinated charging and discharging of EVs is helpful in finding the optimal charging profile and the power demand. It also reduces the electricity costs occurring daily, deviations in voltages, line currents and transformer surgeload [5,7] and also it can obtain the distribution node voltage profile as a flat one [12]. It is identified that the optimal charging and discharging models perceptibly reduce the charging cost by 51 % and 40 % for isolated EV and multiple coordinated vehicles, respectively.…”

Section: Introductionmentioning

confidence: 99%

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ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration

2018

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An Adaptive Neuro Fuzzy Inference System (ANFIS) based Extreme Learning Machine (ELM) theory is utilised in this research work. In particular, the proposed algorithm is applied for designing a controller for electric vehicle to grid (V2G) integration in smart grid scenario. Initially, learning speed and accuracy of this proposed approach are continuously monitored and then, the performance of ELM-ANFIS (e-ANFIS) based controller is examined for its transient response. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Hence, a control practice for their charge and discharge patterns can be easily calculated even with the presence of large numbers of Plug-in Hybrid Electric Vehicles (PHEV). To examine the computational performance and transient response of the e-ANFIS based controller, it is evaluated with the usual ANFIS supported controller. The IEEE 33 bus radial distribution system based approach is implemented to ensure the sturdiness of this prescribed approach.

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“…A variety of recent studies suggest that EVs can contribute to increased peak load, voltage instability, harmonics, and thermal overload [1][2][3][4]. However, if controlled properly, EVs can also be used as tools for demand management, allowing for more efficient use of existing assets and reducing the need for further infrastructure or generation investment.…”

Section: Introductionmentioning

confidence: 99%

Demonstrating Demand Management: How Intelligent EV Charging Can Benefit Everyone

Hoog

Handberg²,

Jegatheesan³

2013

WEVJ

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This paper establishes the case for Electric Vehicle (EV) charging demand management through in-field demonstration, electricity network modelling and financial assessment. As part of the Victorian Government Electric Vehicle Trial, DiUS Computing demonstrated EV charging demand management using United Energy's Smart Grid. Modelling of the United Energy network by the University of Melbourne found that uncontrolled charging would require network augmentation once EVs are adopted by 10% of households. In contrast, managed charging would allow the network to support in excess of 50% uptake using existing capacity and infrastructure. Furthermore, the end-to-end EV charging demand management solution demonstrated by DiUS could be implemented for one tenth the cost of the network augmentation. Although success factors were identified during the demonstration that may serve as an input for demand management program design, electricity market arrangements may be the strongest determinant of adoption generally.

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Distribution transformer stress in smart grid with coordinated charging of Plug-In Electric Vehicles

Cited by 78 publications

References 13 publications

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration

Demonstrating Demand Management: How Intelligent EV Charging Can Benefit Everyone

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