Optimal Planning of charging station for electric vehicle based on particle swarm optimization

Liu, Zifa; Zhang, Wei; Ji, Xing; Li, Ké

doi:10.1109/isgt-asia.2012.6303112

Cited by 114 publications

(67 citation statements)

References 3 publications

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“…It is evident that most of the planning objectives are traditionally aimed to satisfy the EV consumer or the investor [2,3,6,[11][12][13][14][15]. Only scant attention has been given to the power grid aspects in planning studies so far.…”

Section: Electric Vehicle Charging Infrastructure Planningmentioning

confidence: 99%

A comprehensive planning framework for electric vehicle charging infrastructure deployment in the power grid with enhanced voltage stability

Dharmakeerthi

Mithulananthan

Saha

2014

Int. Trans. Electr. Energ. Syst.

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Summary Environmental, economical, and social benefits of the electric vehicle (EV) makes it as a sustainable alternative to fossil fuel driven vehicles. Despite numerous advantages brought by the electrified transportation, it will place a profound impact on the power grids. However, the present EV planning studies have not paid enough attention to the grid impacts, concerns, and priorities. Therefore, a comprehensive multi‐objective EV charging station planning framework is developed, which not only satisfies the EV users and the charging facility investors but which also maintains reliable grid operation. The voltage‐dependent characteristic of the EV load has been taken into consideration. An index that is based on the system reactive power reserve has been introduced to compare relative voltage stability status in different cases. The proposed planning framework is tested and verified by building up a realistic case using EV prediction data and transportation data for the city of Brisbane. Furthermore, the ability of catering for more EV demand with less associated grid impacts is proved by incorporating renewable energy integration. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Electric Vehicle Charging Infrastructure Planningmentioning

confidence: 99%

A comprehensive planning framework for electric vehicle charging infrastructure deployment in the power grid with enhanced voltage stability

Dharmakeerthi

Mithulananthan

Saha

2014

Int. Trans. Electr. Energ. Syst.

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“…Among them, SE, SR, and O often overlap in the commercial area. Therefore, following the method proposed in [10,40,41], we classify the study area into the residential area (H), industrial area (W), and commercial area (SE/SR/O).…”

Section: Destination Of Tripmentioning

confidence: 99%

“…The fixed investment cost for the charging stations at different levels rated according to the number of fast chargers after sizing is shown in Table 3 [40]. Land rental cost and time cost in different areas can be found in Table 4.…”

Section: Residential Areamentioning

confidence: 99%

Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route

Zang

Chen³

et al. 2018

Applied Sciences

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Abstract:The major factors affecting the popularization of electric vehicles (EV) are the limited travel range and the lack of charging infrastructure. Therefore, to further promote the penetration of EVs, it is of great importance to plan and construct more fast charging stations rationally. In this study, first we establish a travel pattern model based on the Monte Carlo simulation (MCS). Then, with the traveling data of EVs, we build a bi-level planning model of charging stations. For the upper model, with an aim to maximize the travel success ratio, we consider the influence of the placement of charging stations on the user's travel route. We adopt a hybrid method based on queuing theory and the greedy algorithm to determine the capacity of charging stations, and we utilize the total social cost and satisfaction index as two indicators to evaluate the optimal solutions obtained from the upper model. Additionally, the impact of the increase of EV ownership and slow charger coverage in the public parking lot on the fast charging demands and travel pattern of EV users are also studied. The example verifies the feasibility of the proposed method.

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“…Novel methods that deal with the optimal charging of electric vehicles, in static or dynamic mode, have recently been introduced. These vary from stationary stations that are scattered across the road network in central positions [43][44][45], dynamic wireless charging methods that take advantage of the mobility of nodes [46,47] and eco-routing algorithms that run in isolation in every vehicle or in a central way for a fleet of vehicles [48,49]. The authors in [50] present a method for dynamic wireless charging of vehicles based on the concept that vehicles meet at rendezvous points, giving a social aspect to the power transfer procedure.…”

Section: Electric Vehiclesmentioning

confidence: 99%

Social Internet of Vehicles for Smart Cities

Μαγλαράς

Al-Bayatti

et al. 2016

JSAN

138

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Digital devices are becoming increasingly ubiquitous and interconnected. Their evolution to intelligent parts of a digital ecosystem creates novel applications with so far unresolved security issues. A particular example is a vehicle. As vehicles evolve from simple means of transportation to smart entities with new sensing and communication capabilities, they become active members of a smart city. The Internet of Vehicles (IoV) consists of vehicles that communicate with each other and with public networks through V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure) and V2P (vehicle-to-pedestrian) interactions, which enables both the collection and the real-time sharing of critical information about the condition on the road network. The Social Internet of Things (SIoT) introduces social relationships among objects, creating a social network where the participants are not humans, but intelligent objects. In this article, we explore the concept of the Social Internet of Vehicles (SIoV), a network that enables social interactions both among vehicles and among drivers. We discuss technologies and components of the SIoV, possible applications and issues of security, privacy and trust that are likely to arise.

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Optimal Planning of charging station for electric vehicle based on particle swarm optimization

Cited by 114 publications

References 3 publications

A comprehensive planning framework for electric vehicle charging infrastructure deployment in the power grid with enhanced voltage stability

A comprehensive planning framework for electric vehicle charging infrastructure deployment in the power grid with enhanced voltage stability

Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route

Social Internet of Vehicles for Smart Cities

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