Stochastic modeling of electric vehicle movable loads: Nodal impact from transportation

Tang, Difei; Wang, Peng

doi:10.1109/pmaps.2014.6960647

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Prediction of spatiotemporal availability of EVs with their SOC is very important for effective management of power distribution. Research has been conducted to forecast the spatiotemporal availability, charging demand, respective load profiles and peak shaving potential of EVs [19][20][21][22][23][24][25][26]. However, the impact on existing power grid was not analysed using real travel data.…”

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confidence: 99%

Potential for electric vehicle adoption in Australia

Rafique

Town

2018

International Journal of Sustainable Transportation

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Transport is expected to become electrified in coming decades, bringing new challenges and opportunities for commuters and electricity distributors. This thesis presents analysis of Household Travel Survey (2014/15) and Journey to Work (2011) census datasets from the New South Wales (NSW) with the aim of;(i) investigating whether electric vehicles (EVs) could meet the daily commuting needs, and(ii) quantifying the potential impact of EVs on the electricity distribution grid as a function of location and time.It was found that 87% of commuter vehicle trips could be provided using affordable EVs and that the resulting electricity demand would increase by more than 10% in only 9 out of 35 local government areas (LGAs) in NSW, Australia. We also quantified the potential spatiotemporal electric energy available for vehicle-to-grid services.It was found that greenhouse gas emissions across NSW would reduce by 26% CO2(eq) even if all EVs were recharged from non-renewable coal-fired power plants, due to greater efficiency of EVs. The results demonstrated the potential for wide-scale adoption of EVs in Australia. Lastly, to facilitate analysis and prediction of key variables, the travel data was modelled using regression trees (RTs) and artificial neural networks (ANNs).

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confidence: 99%

Potential for electric vehicle adoption in Australia

Rafique

Town

2018

International Journal of Sustainable Transportation

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“…• State-of-charge (SOC): This is the state of the battery at a given moment, assuming that the battery is fully charged after ending the charging processes; the SOC when the charging processes ends is 100%, and it decreases as the vehicle is used. Equation ( 2) is adapted from [18] and describes how to calculate SOC from daily mileage (DM), EV performance (P EV ), and battery capacity (C).…”

Section: Charging Behaviormentioning

confidence: 99%

“…From these parameters it is possible to determine the number of charging hours for each user and the starting time of charge. Charging time (H) depends on residual battery power (SOC), charger efficiency (η), battery capacity (C), and the rated power capacity of the charger (P charger ), as can be seen in Equation ( 3), which is adapted from [18].…”

Section: Charging Behaviormentioning

confidence: 99%

Methodology to Evaluate the Impact of Electric Vehicles on Electrical Networks Using Monte Carlo

et al. 2021

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In preparation for the electric mobility technological transition in Colombia, an impact assessment of the electric power system is required, considering the increasing loading that must be able to be managed in the future. In this paper, a plug-in electric vehicle (PEV) charging simulation methodology is developed in order to dimension the impact of this type of load on power grids. PEV electric properties, user charging behaviors, geographic location, trip distances, and other variables of interest are modeled from empirical or known probability distributions and later evaluated in different scenarios using Monte Carlo simulation and load flow analysis. This methodology is later applied to the transmission network of Antioquia (a department of Colombia) resulting in load increases of up to 40% on transmission lines and 20% on transformers in a high PEV penetration scenario in 2030, increases that are well within the expected grid capacity for that year, avoiding the need for additional upgrades.

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