Two-Stage Mechanism for Massive Electric Vehicle Charging Involving Renewable Energy

Wang, Ran; Wang, Ping; Xiao, Gaoxi

doi:10.1109/tvt.2016.2523256

Cited by 116 publications

(80 citation statements)

References 39 publications

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“…• Schedule and control the charging/discharging of EVs [7] (depending on different charging technologies, such as normal and fast charging), with different durations such that power grid constraints are maintained. This benefits power grid such that peaks and possible overloads of the electricity network may be avoided.…”

Section: A Parking Modementioning

confidence: 99%

A Cost-Efficient Communication Framework for Battery-Switch-Based Electric Vehicle Charging

et al. 2017

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Abstract-Charging management for Electric Vehicles (EVs) on-the-move has become an increasingly important research problem in smart cities. Major technical challenges include the selection of Charging Stations (CSs) to guide charging plans, and the design of cost-efficient communication infrastructure between the power grid and EVs. In this article, we first present a brief review on state-of-the-art EV charging management schemes. Next, by incorporating battery switch technology to enable fast charging service, a Publish/Subscribe (P/S) communication framework is provisioned to support the EV charging service. Upon that, we develop a fully distributed charging management scheme with the consideration of urban travel uncertainties, e.g., traffic congestions and drivers' preferences. This would benefit from low privacy sensitivity, as EVs' status information will not be released through management. Results demonstrate a guidance for the provisioning of P/S communication framework to improve EV drivers' experience, e.g., charging waiting time and total trip duration. Also, the benefit of P/S communication framework is reflected in terms of the communication efficiency. Open research issues of this emerging area are also presented.

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Section: A Parking Modementioning

confidence: 99%

A Cost-Efficient Communication Framework for Battery-Switch-Based Electric Vehicle Charging

et al. 2017

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“…In [1], a novel two-stage EV charging mechanism is designed to reduce the energy cost and PAR of the power grid with highly fluctuant renewable energy as a significant portion of the power resources. Reference [2] proposes an Integer Linear Programming (ILP) based optimization technique to minimize the peak hourly load by scheduling household appliances and a sizable number of EV vehicles connected to the grid.…”

Section: Introductionmentioning

confidence: 99%

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

Zhang

Leng

et al. 2016

2016 IEEE/CIC International Conference on Communications in China (ICCC)

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Abstract-The frequency, time and places of charging have large impact on the Quality of Experience (QoE) of EV drivers. It is critical to design effective EV charging scheduling system to improve the QoE of EV drivers. In order to improve EV charging QoE and utilization of CSs, we develop an innovative travel plan aware charging scheduling scheme for moving EVs to be charged at Charging Stations (CS). In the design of the proposed charging scheduling scheme for moving EVs, the travel routes of EVs and the utility of CSs are taken into consideration. The assignment of EVs to CSs is modeled as a two-sided manyto-one matching game with the objective of maximizing the system utility which reflects the satisfactory degrees of EVs and the profits of CSs. A Stable Matching Algorithm (SMA) is proposed to seek stable matching between charging EVs and CSs. Furthermore, an improved Learning based On-LiNe scheduling Algorithm (LONA) is proposed to be executed by each CS in a distributed manner. The performance gain of the average system utility by the SMA is up to 38.2% comparing to the Random Charging Scheduling (RCS) algorithm, and 4.67% comparing to Only utility of Electric Vehicle Concerned (OEVC) scheme. The effectiveness of the proposed SMA and LONA is also demonstrated by simulations in terms of the satisfactory ratio of charging EVs and the the convergence speed of iteration.

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“…For instance, all the models above assume that the aggregated charging power of a large EV population at time t is the multiplication of N and average charging power (μ C ) as in (2.8) [67].…”

Section: Objective 1: Modelling Ev Loadmentioning

confidence: 99%

“…Travel distances are known [138,141] Its empirical distribution is a given [64] First and last trip times modeled by time-invariant Normal distributions [67,137] Charging start time modeled by a time-invariant Normal distribution [53] Exact charging start time is known [64,83] Individual SOC, i.e., x k+ is in X k+ , is updated after every timeslot [17,136,138,145] …”

Section: Nature Of the Data Referencesmentioning

confidence: 99%

“…Finds the time-slots in which each of the n k+ EVs need to be charged [31,93,155] Calculates the urgency factor of charging [31,67,155] The least laxity first (LLF) algorithm calculates the laxity [34,157,158] A SOC and laxity-based charging/discharging [34] To overcome the drawback, the authors in [34] have proposed a SOC and laxity-based charging/discharging. It divides the entire SOC-laxity plane into several groups as in Fig.…”

Section: Charging Strategies Referencesmentioning

confidence: 99%

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PV-based EV charging station with vehicle-to-grid services for business premises

Islam¹

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The transportation sector is the second major contributor to the global greenhouse gas (GHG) emissions, next to electricity generation sector. GHG emissions are projected to grow in the future due to the over-reliance on fossil fuels for both electricity generation and transportation sectors.However, the growth can be stalled by gradually introducing electric vehicles (EVs) as they produce less GHG compared to their conventional counterparts. Despite that, their proliferation has been stunted by a completely new set of challenges in addition to a very high capital cost. These challenges include low driving ranges, scarcity of electric vehicle charging stations (EVCS), long charging time and inability to provide charging service readily. Nevertheless, some of these challenges can be addressed by installing EVCS at business premises (i.e., offices, universities, etc.) and augmenting EVCS with onsite PV and battery energy storage (BES). In that case, the impacts of EV charging on the grid will be less significant; charging services will be available readily, and GHG emissions growth can be reduced significantly. Moreover, EVCS, under suitable conditions, would be able to provide vehicle-to-grid (V2G) services, which makes EVCS more attractive.Despite such prospective opportunities, offhand planning and operational planning of EVCS would deem them unachievable. In addition, the inherent intermittency of PV, and EV and grids loads will pose considerable hindrance on availing those benefits.Hence, this research focusses on an EVCS with onsite PV and BES, in the perspective of planning and operational planning including V2G services. The cornerstone of the planning and operational planning is an appropriate EV load model that captures the uncertainties of EV charging. In addition, the EV load model reflects the grid voltage and EV battery's state of charge (SOC)-dependency of EV charging. Moreover, it accounts for the diversity of the EV population embodied by market shares, battery sizes, charging levels, and charging voltages, currents, and power factors. Furthermore, it is scalable to facilitate seamless assimilation of a large EV population. Therefore, a new EV load model is first proposed with MATLAB/Simulink validation reflecting the factors above along with the recommendations by the standard BS EN 61851-23:2014. This EV model is then incorporated into the planning activities, which include four chronological exercises. Firstly, the impact (i.e., grid voltage, current, losses, etc.) of the EV charging on the grid at different locations, namely home, office, public charging, is investigated considering the uncertainties. Secondly, the optimal location of the PV and BES based EVCS is divulged regarding the reduction of the impact and costs involved while enhancing the charging quality of service (QoS). Thirdly, a suitability analysis is performed for the obtained optimal location to find the most suitable combination of the grid upgrading, PV deployment, and BES iii deployment to satisfy the QoS, ...

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Two-Stage Mechanism for Massive Electric Vehicle Charging Involving Renewable Energy

Cited by 116 publications

References 39 publications

A Cost-Efficient Communication Framework for Battery-Switch-Based Electric Vehicle Charging

A Cost-Efficient Communication Framework for Battery-Switch-Based Electric Vehicle Charging

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

PV-based EV charging station with vehicle-to-grid services for business premises

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