Influence of EV on grid power quality and optimizing the charging schedule to mitigate voltage imbalance and reduce power loss

Singh, Mukesh; Kar, Indrani; Kumar, Praveen

doi:10.1109/epepemc.2010.5606657

Cited by 63 publications

(35 citation statements)

References 5 publications

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“…Conversely, more attention is focused on the technique of energy storage rather than the theory of V2G and also, the model is indented only for a transmission network. The consequences of connecting EVs on the distribution grid using load flow techniques and also the performances of the system have been examined [13,15]. But, all these efforts have not employed any control technique for the charging or discharging of EVs in E2G mode.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

2018

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“…A decentralised voltage dependent charging strategy, which requires only local voltage measurements, can be used for mitigating the low EV-induced voltages [18], [19]. That is, EV charging power can be modulated in accordance to local voltage measurements in order to compensate the voltage unbalances and improve the overall power quality [20], [21].…”

mentioning

confidence: 99%

Management of Power Quality Issues in Low Voltage Networks Using Electric Vehicles: Experimental Validation

Martinenas

Knezović

Marinelli

2017

IEEE Trans. Power Delivery

113

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Abstract-As Electric Vehicles (EVs) are becoming more wide spread, their high power consumption presents challenges for the residential low voltage networks, especially when connected to long feeders with unevenly distributed loads. However, if intelligently integrated, EVs can also partially solve the existing and future power quality problems. One of the main aspects of the power quality relates to voltage quality. The aim of this work is to experimentally analyse whether series-produced EVs, adhering to contemporary standard and without relying on any V2G capability, can mitigate line voltage drops and voltage unbalances by a local smart charging algorithm based on a droop controller. In order to validate this capability, a low-voltage grid with a share of renewable resources is recreated in SYSLAB PowerLabDK. The experimental results demonstrate the advantages of the intelligent EV charging in improving the power quality of a highly unbalanced grid.

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“…EVs represent a significant load increase to the system and without control they may cause stability problems and lead to an undesirable grid operation state [1]. However, EVs may also benefit the grid and improve its operation, e.g., by supplying regulation power [2].…”

Section: Introductionmentioning

confidence: 99%

Alternating Direction Method of Multipliers for decentralized electric vehicle charging control

Rivera

Wolfrum

Hirche³

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-The integration of Electric Vehicles (EVs) into the power grid is a challenging task. From the control perspective, one of the main challenges is the definition of a comprehensive control structure that is scalable to large EV numbers. This paper makes two key contributions: (i) It defines the EV ADMM framework for decentralized EV charging control. (ii) It evaluates EV ADMM using actual data and various EV fleet control problems. EV ADMM is a decentralized optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM). It separates the centralized optimal fleet charging problem into individual optimization problems for the EVs plus one aggregator problem that optimizes fleet goals. Since the individual problems are coupled, they are solved consistently by passing incentive signals between them. The framework can be parameterized to trade-off the importance of fleet goals versus individual EV goals, such that aspects like battery lifetime can be considered. We show how EV ADMM can be applied to control an EV fleet to achieve goals such as demand valley filling and minimal-cost charging. Due to its flexibility and scalability, EV ADMM offers a practicable solution for optimal EV fleet control.

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Influence of EV on grid power quality and optimizing the charging schedule to mitigate voltage imbalance and reduce power loss

Cited by 63 publications

References 5 publications

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

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

Management of Power Quality Issues in Low Voltage Networks Using Electric Vehicles: Experimental Validation

Alternating Direction Method of Multipliers for decentralized electric vehicle charging control

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