Driving Profile Generator

Dietz, Benjamin; Ahlert, Klaus-Henning; Block, Carsten

doi:10.2139/ssrn.1652753

Cited by 3 publications

(3 citation statements)

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“…A sudden jump in vehicle population on the road usually occurs before and after working hours. The average distance justifies the above hypothesis by travelled data of German transportation as reported in [19].…”

Section: Mathematical Modellingsupporting

confidence: 84%

“…ϑ k, L and ϑ k, U are fraction of lower and upper limit of Kth vehicle's battery to its maximum energy capacity E k, max Bat , respectively. Using charging and discharging (17) and (18), the charging (τ k, cha ) and discharging (τ k, dis ) time constant can be written as in (19) and (20), respectively.…”

Section: V2g Energy Exchange Modelmentioning

confidence: 99%

“…

N_{M}^{i} \leq N

. Distance travelled (

D_{i} false(k false)

) by

k th

vehicle is generated by data reported in [19]. The speed profile (5) of the vehicle fleet is formed by generating sets of random numbers

r false[0, 1 false]

which satisfy (6).…”

Section: Mathematical Modellingmentioning

confidence: 99%

See 2 more Smart Citations

Potential impacts of emission control policy on the vehicle to grid environment: a novel approach

Ahmad

Sivasubramani

2018

IET Smart Grid

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Environmental conditions strained the existing transportation network to replace its conventional gasoline-based engines by electric vehicles (EVs) to reduce its share in pollution. EVs promise a potential solution, but they fail to motivate end users because of their inherent drawbacks. Utilisation of EVs in vehicle-to-grid (V2G) environment is expected to boost EVs penetration but will burden the conventional power generations for their energy demand. For improvement of the environmental conditions, it is likely that the authorities will come up with a strict emission control policy. This study demonstrates the effects of emission policy on the V2G environment. The proposed problem is modelled as a multi-objective optimisation problem. Nondominated-sorting-based genetic algorithm (NSGA-II) is used to solve this problem. This study also demonstrates a long-term impact analysis of a system consisting of a V2G market, end users and EVs by considering time variance in EVs penetration and vehicles' performance degradation. This analysis can help in planning and formulating the future V2G market.

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Section: Mathematical Modellingsupporting

confidence: 84%

Section: V2g Energy Exchange Modelmentioning

confidence: 99%