Stochastic Modeling for Studies of Real-World PHEV Usage: Driving Schedule and Daily Temporal Distributions

Lee, Taekyung; Bareket, Z; Gordon, Timothy; Filipi, Zoran

doi:10.1109/tvt.2011.2181191

Cited by 131 publications

(62 citation statements)

References 10 publications

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“…The daily driving requirements of vehicle drivers were analyzed with GPS-based driving information collected from 484 vehicles for in Atlanta of the United States in [37]. Reference [38] used the real-world driving data that comprise 4409 trips in Southeast Michigan of the United States to build a model of the daily driving mission for studies of real-world PHEV usage. It is developed a driving pattern recognition method for EV range estimation in [39].…”

Section: Introductionmentioning

confidence: 99%

Driving pattern analysis of Nordic region based on National Travel Surveys for electric vehicle integration

Liu

Christensen

et al. 2015

J. Mod. Power Syst. Clean Energy

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Electic vehicles (EVs) show great potential to cope with the intermittency of renewable energy sources (RES) and provide demand side flexibility required by the smart grid. Furthermore, EVs will increase the electricity consumption. Large scale integration of EVs will probably have substantial impacts on power systems. This paper presents a methodology to transform driving behavior of person into one of the cars in order to analyze the driving pattern of EVs based on the National Travel Surveys. In the proposed methodology, a statistical process is used to obtain the driving behavior of cars by grouping the survey respondents according to the driving license number and car number, and mapping the households with similar characteristics. The proposed methodology was used to carry out the driving pattern analysis in the Nordic region. The detailed driving requirements and charging/discharging availability of vehicles along the day were obtained.Two types of EV availabilities were studied in this paper considering different charging/discharging conditions of EVs for the power system integration, i.e. EV availability all day and EV availability at home. The results show that the daily driving requirements of the Nordic region are not very intensive. The driving patterns of vehicles in the Nordic region vary on weekdays and weekends. The two types of EV availabilities are quite different from each other.

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Section: Introductionmentioning

confidence: 99%

Driving pattern analysis of Nordic region based on National Travel Surveys for electric vehicle integration

Liu

Christensen

et al. 2015

J. Mod. Power Syst. Clean Energy

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“…where i indicates the type of PEVs, j is number of PEVs, L j is the trip path for jth PEV, and L max i is the rated length path that each type of PEVs can trip [29]. In this paper, the selected values for parameters 1 , ˛2 and ˛3 are 0.85, 0.8, and 0.75, ˇ1, ˇ2 and ˇ3 are 0.15, 0.2 and 0.25; and L 1 , L 2 and L 3 are 40, 50, 60 miles, respectively.…”

Section: Problem Formulationmentioning

confidence: 99%

Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization

Hajforoosh

Masoum

Islam

2015

Electric Power Systems Research

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“…The SOC quantifies the energy requirement to fully charge all PEVs so that the grid operator can schedule appropriate power generation to accommodate the PEV charging need, and the plug-in/plug-off time prioritizes the PEV fleet and determines which vehicle receives immediate or delayed charging service. The data in [31], shown in Fig. 1, is used to derive the three pieces of information.…”

Section: A Plug-in Electric Vehicle Fleetmentioning

confidence: 99%

“…1. Distributions of the plug-in time, plug-off time, and trip length [31]. The distribution of trip length is used to derive .…”

Section: Introductionmentioning

confidence: 99%

Synergistic control of plug-in vehicle charging and wind power scheduling

Ahn

Peng

et al. 2013

IEEE Trans. Power Syst.

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Abstract-Significant synergy exists between plug-in electric vehicles (PEVs) and wind energy: PEVs can be the demand response to mitigate the intermittent wind power outputs, and wind energy can provide low-carbon electricity to PEVs. This paper presents a hierarchical control algorithm to realize this synergy by integrating the PEV charging and wind power scheduling. The control algorithm consists of three levels: the top-level controller optimizes the scheduling for the conventional power plants and wind power; the middle-level controller plans PEV charging to achieve load following based on the battery state of charge and plug-off time of each vehicle; the bottom-level controller uses grid electricity frequency as the feedback cue to control PEV charging and serves as the ancillary service to the grid. Numerical simulations show that the integrated controller can improve the grid frequency regulation and overall electricity generation cost without sacrificing the PEVs charging completion.

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Stochastic Modeling for Studies of Real-World PHEV Usage: Driving Schedule and Daily Temporal Distributions

Cited by 131 publications

References 10 publications

Driving pattern analysis of Nordic region based on National Travel Surveys for electric vehicle integration

Driving pattern analysis of Nordic region based on National Travel Surveys for electric vehicle integration

Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization

Synergistic control of plug-in vehicle charging and wind power scheduling

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