Fuel reduction and electricity consumption impact of different charging scenarios for plug-in hybrid electric vehicles

Zhang, Li; Brown, Tim; Samuelsen, G. S.

doi:10.1016/j.jpowsour.2011.03.003

Cited by 82 publications

(41 citation statements)

References 11 publications

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“…Valsera et al use the power ratio of Mitsubishi i-MiEV when the initial SoC is 20% and the EV needs 4 h to reach 100%. Zhang et al [32] use level 1 (120 V-15 or 20 A) in the studio located in the United States. To compare, Grenier et al [33] use 230 V and 15 A and the study is located in New Zealand.…”

Section: Battery and Charging Processmentioning

confidence: 99%

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Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks

et al. 2015

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Electric Vehicles (EVs) have seen significant growth in sales recently and it is not clear how power systems will support the charging of a great number of vehicles. This paper proposes a methodology which allows the aggregated EV charging demand to be determined. The methodology applied to obtain the model is based on an agent-based approach to calculate the EV charging demand in a certain area. This model simulates each EV driver to consider its EV model characteristics, mobility needs, and charging processes required to reach its destination. This methodology also permits to consider social and economic variables. Furthermore, the model is stochastic, in order to consider the random pattern of some variables. The model is applied to Barcelona's (Spain) mobility pattern and uses the 37-node IEEE test feeder adapted to common distribution grid characteristics from Barcelona. The corresponding grid impact is analyzed in terms of voltage drop and four charging strategies are compared. The case study indicates that the variability in scenarios without control is relevant, but not in scenarios with control. Moreover, the voltages do not reach the minimum voltage allowed, but the MV/LV substations could exceed their capacities. Finally, it is determined that all EVs can charge during the valley without any Energies 2015, 8 4161 negative effect on the distribution grid. In conclusion, it is determined that the methodology presented allows the EV charging demand to be calculated, considering different variables, to obtain better accuracy in the results.

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Section: Battery and Charging Processmentioning

confidence: 99%

“…Some authors employ the NHTS (National Household Travel Survey) to analyse the United States, such as [21,32,36,[43][44][45]. In the United Kingdom, studies use NTS (National Travel Survey) and UKTUS (United Kingdom Time Use Survey), for instance [3,46,47].…”

Section: Mobilitymentioning

confidence: 99%

Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks

et al. 2015

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“…Moreover, the tendency to plug the vehicle in as soon as they reach home is high. Numerous studies like [6]- [10] highlight the impact of EVs on electricity distribution in the future, in elaborate detail. Whilst the recharging load for a small number of EVs is likely to be buried in the baseline load fluctuation, a large fleet of EVs charging at the same time could have an overwhelming effect on the grid during peak hours [11].…”

Section: Introductionmentioning

confidence: 99%

Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging

Mody¹,

Steffen²

2017

SAE Technical Paper Series

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The goal of grid friendly charging is to avoid putting additional load on the electricity grid when it is heavily loaded already, and to reduce the cost of charging to the consumer. In a smart metering system, Day Ahead tariff (DA) prices are announced in advance for the next day. This information can be used for a simple optimization control, to select to charge at cheapest times. However, the balance of supply and demand is not fully known in advance and the Real-Time Prices (RTP) are therefore likely to be different at times. There is always a risk of a sudden price change, hence adding a stochastic element to the optimization in turn requiring dynamic control to achieve optimal time selection. A stochastic dynamic program (SDP) controller which takes this problem into account has been made and proven by simulation in a previous paper.Since there are differences between the DA and the RTP tariff, this paper proposes a (1) predictor to create an unbiased estimate of the RTP tariff based on available data. It uses a regression on historical data to find the best prediction of the expected price. Finally, a (2) case study based on data from the Illinois Electricity Grid prices is presented to validate the SDP controller over several years of data. The stochastic optimization uses the RTP prices effectively, getting very close to the globally optimal charging price. However, the predictor achieves only a slight reduction in prediction uncertainty with this data sate, and it has a negligible effect on cost. This means that DA prices can be used as a fair prediction of RTP charging cost here. The SDPM successfully reacts in the case study and leads to savings on charging costs over the years presented.

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“…Weiller (2011) concluded that non-home charging increases daily electric energy use of PHEVs from 24% to 29% (1.5-2 kWh/day) [10]. The delayed and average charging strategies can have the same fuel reduction as opportunity charging, but have different instances electricity consumption impacts [11]. Delayed charging at home-related locations can move PHEV charging from peak hours to off-peak and decrease the PHEV demand peak load by 50%.…”

Section: Introductionmentioning

confidence: 99%

Charge Management Optimization for Future TOU Rates

Zhang

Markel

2016

WEVJ

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SummaryThe effectiveness of future time of use (TOU) rates to enable managed charging depends on the vehicle's flexibility and the benefits to owners. This paper adopts opportunity, delayed, and smart charging methods to quantify these impacts, flexibilities, and benefits. Simulation results show that delayed and smart charging methods can shift most charging events to lower TOU rate periods without compromising the charged energy and individual driver mobility needs.

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Fuel reduction and electricity consumption impact of different charging scenarios for plug-in hybrid electric vehicles

Cited by 82 publications

References 11 publications

Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks

Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks

Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging

Charge Management Optimization for Future TOU Rates

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