An energy transmission and distribution network using electric vehicles

Yi, Ping; Zhu, Ting; Jiang, Bo; Wang, Bing; Towsley, Don

doi:10.1109/icc.2012.6364483

Cited by 47 publications

(32 citation statements)

References 20 publications

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“…Unless otherwise stated, we assume the battery of a bus can store 20 units of energy. The energy transfer loss while exchanging energy is very low (less than 10% [14]). For simplicity, we assume no energy transfer loss (the results are similar when assuming energy transfer loss of 10%).…”

Section: A Simulation Settingmentioning

confidence: 96%

“…1(a), charge stations are placed at interchange points (bus stops) that connect two or more routes. We have studied charge station placement in [14]. Now with the locations of charge stations fixed, another important problem is how to transfer renewable energy effectively among buses.…”

Section: Problem Settingmentioning

confidence: 99%

“…Charge stations should be placed so that the number of charge stations is minimized and buses on a bus route that does not have a renewable energy source can indirectly use renewable energy through charge stations. We use the two schemes, greedy and random schemes, proposed in our prior work [14] for charge station placement. Specifically, the greedy scheme picks a bus stop from the map that covers the largest number of bus lines as a charge station and adds it to a list.…”

Section: A Simulation Settingmentioning

confidence: 99%

“…In our prior work [14], we propose a novel concept, EV energy network, for energy distribution and transmission using EVs. Specifically, an EV energy network consists of a set of EVs and EV charge stations as well as an energy transportation network.…”

Section: Introductionmentioning

confidence: 99%

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Optimal renewable energy transfer via electrical vehicles

Arikan

Jin

Wang

et al. 2015

2015 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Using electrical vehicles (EV) in transportation systems is more cost effective and environmental friendly than using conventional vehicles. In addition, the energy storage capability and mobility of EVs provide a convenient way to transfer energy from renewable energy sources to locations that have no direct access to renewable energy. As an example, an EV can be charged by a renewable energy source and discharge energy at a charge station; other EVs passing by the charge station can get charged, and hence indirectly use the energy from the renewable energy source. In this paper, we investigate the optimal renewable energy transfer problem in a bus system. Specifically, the goal is to determine how much energy a bus should deposit or withdraw at a charge station so that the total amount of renewable energy used by the bus system is maximized. We formulate and solve the above optimization problem using linear programming. Simulation results using the Manhattan city bus system demonstrate that our approach significantly outperforms a baseline scheme and provides an effective way for distributing renewable energy in bus systems.

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Section: A Simulation Settingmentioning

confidence: 96%

Section: Problem Settingmentioning

confidence: 99%

Section: A Simulation Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal renewable energy transfer via electrical vehicles

Arikan

Jin

Wang

et al. 2015

2015 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, [33] provides a transmission network based on the capability Internet of vehicles to transfer energy to the regions of high energy consumption. This way, the required upgrades will be deferred and occur gradually over time.…”

Section: Opportunitiesmentioning

confidence: 99%

A survey on communication technologies and requirements for internet of electric vehicles

Bayram

Papapanagiotou

2014

J Wireless Com Network

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Electric vehicles (EVs) are becoming a more attractive transportation option, as they offer great cost savings, decrease foreign oil dependency, and reduce carbon emissions. However, varying temporal and spatial demand patterns of EVs threatens power grid operations and its physical components. Thus, the ability of the power grid to handle the potential extra load has become a major factor in the mainstream success. In order for this integration to occur seamlessly, the power grid and the consumers need to be coordinated in harmony. In this paper, we address the critical challenges introduced by the penetration of EVs, systematically categorize the proposed frameworks for demand management, and the role of information and communication technologies in the solution process. We provide a comprehensive survey on the communication requirements, the standards and the candidate technologies towards the Internet of electric vehicles (IoEV). This survey summarizes the current state of research efforts in electric vehicle demand management and aims to shed light on the continued studies.

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Multi‐objective optimization for optimal energy transporting path and energy distribution in electric vehicles energy internet

Ramasamy,

Vedi,

Madasamy

2024

Int J Communication

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SummaryEnergy internet permits the power to stream lithely for broadcast and it aids in transporting energy to each user using electric vehicles (EVs). The energy that EVs utilize is generated using certain sources. Still, considering poor weather, the power station averts the production of energy and needs to acquire power transmitted to another location. One resolution is to set stations that aid to save surfeit energies and minimize energy loss during transportation. This paper devises a new model for choosing charging stations to charge EVs. Energy is transferred from the energy source to the charge station via bus station through optimal path using Battle Royale Jaya Optimization (BRJO) routing with fitness attributes like distance to select the shortest path and energy. The prediction of energy is done with a deep recurrent neural network (DRNN) and the charge is distributed optimally to EVs by employing a proposed Competitive Swarm‐Battle Royale Jaya Optimization (CSBRJO) by utilizing multi‐parameters, like priority, distance, predicted energy, waiting time, and EV count needed for charging. The CSBRJO outperformed with the smallest distance of 6.935 km, energy consumption of 1.676 J, path length of 5.05 m, and waiting time of 4.645 sec.

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An energy transmission and distribution network using electric vehicles

Cited by 47 publications

References 20 publications

Optimal renewable energy transfer via electrical vehicles

Optimal renewable energy transfer via electrical vehicles

A survey on communication technologies and requirements for internet of electric vehicles

Multi‐objective optimization for optimal energy transporting path and energy distribution in electric vehicles energy internet

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