The PHEV Charging Scheduling and Power Supply Optimization for Charging Stations

Dong, Qiumin; Niyato, Dusit; Wang, Ping; Han, Zhu

doi:10.1109/tvt.2015.2399411

Cited by 56 publications

(34 citation statements)

References 26 publications

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“…Year Objectives Opt-Methods Tools or platforms Sundstrom and Binding [76] 2010 Operational cost minimisation LP CPLEX Clement-Nyns et al [110] 2011 Operational cost minimisation LP Not stated Sortomme and El-Sharkawi [85] 2012 Welfare maximisation LP Matlab CVX Jin et al [106] 2013 Operational cost minimisation LP CPLEX Fernandez et al [92] 2013 Battery cost minimisation LP GAMS,CPLEX Xu et al [119] 2014 Operational cost minimisation LP Matlab CVX Salah et al [120] 2015 Operational cost minimisation LP CPLEX Battistelli et al [78] 2012 Operational cost minimisation Robust LP GAMS,CPLEX Dong et al [122] 2015 Operational cost minimisation Stochastic LP GAMS,CPLEX Sundstrom and Binding [96] 2012 Difference minimisation Modified LP CPLEX before scheduling and a dynamic situation that the PEV may join or leave the charging every hour are modelled and solved. In Arslan and Karasan [93], LP was adopted to minimise battery degradation.…”

Section: Authors and Refmentioning

confidence: 99%

“…Robust optimisation is more realistic and conservatively configured for the practical case study, however in the cost of the sacrificing the precision of the optimal solutions. Dong et al [122], on the other hand, employed stochastic programming to model uncertainty behaviours of PEV users. Table 1 summarises some researches in LP methods in terms of PEV scheduling, associated with the published year, objectives as well as the optimisers and platforms for the problem solving.…”

Section: Authors and Refmentioning

confidence: 99%

See 1 more Smart Citation

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Yang

Foley

2015

Renewable and Sustainable Energy Reviews

198

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Section: Authors and Refmentioning

confidence: 99%

Section: Authors and Refmentioning

confidence: 99%

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Yang

Foley

2015

Renewable and Sustainable Energy Reviews

198

View full text Add to dashboard Cite

“…We propose a stochastic model based on Markov arrival processes (MAP) to capture the nature of electric car charging process. The only paper we found using MAP is [10], where the authors use a DMAP/PH/N/R model (where DMAP stands for discrete time MAP and PH for phase type distributed service time) to investigate a battery replacement strategy to increase the efficiency of chargers and save drivers' time at charging stations.…”

Section: Introductionmentioning

confidence: 99%

Capacity Planning of Electric Car Charging Station Based on Discrete Time Observations and MAP(2)/G/c Queue

Farkas

Telek

2018

Period. Polytech. Elec. Eng. Comp. Sci.

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The modeling of electric car charging stations is essential for determining the required number of chargers in order to ensure the required service quality. In this paper we propose a new estimation method for the stochastic modeling of electric car charging stations, based on Markov arrival process (MAP).The input of the proposed model is empirical data for the arrival and service process of electric cars, given as histograms: the number of arriving cars during a fixed time slot (5 minutes in our case) and the histogram of service times (in 5 minutes granularity). The fact that observations on the continuous time process of car charging are available in discrete time steps poses a modeling challenge, which was not considered before. We propose a procedure to fit the observed data with a continuous time MAP of order 2 such that three moments and a correlation parameter of the discrete time observations are matched with three moments and the correlation parameter of the continuous time MAP for the given time interval. We implemented the fitting procedure in MATLAB and verified the obtained model of car charging station against simulation. As the MAP model of the arrival processes is reasonably close to the observations, the obtained MAP/G/c queue allows a more accurate dimensioning of car charging station than the previously applied ones.

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“…In recent years, plug-in hybrid electric vehicles (PHEVs), as eco-friendly and cost-effective transportation vehicles [1], have drawn world-wide attention [2,3]. By shifting from fuel power to battery power, electric driven vehicles are not only helpful to reduce the emission of green-house gas but also have a considerable energy independence.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

et al. 2017

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Abstract:To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner's historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.

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The PHEV Charging Scheduling and Power Supply Optimization for Charging Stations

Cited by 56 publications

References 26 publications

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Capacity Planning of Electric Car Charging Station Based on Discrete Time Observations and MAP(2)/G/c Queue

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

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