Optimal Incentive Plans for Plug-in Electric Vehicles

Rahmani-Andebili, Mehdi; Firuzabad, Mahmud Fotuhi; Moeini‐Aghtaie, Moein

doi:10.1007/978-981-10-7056-3_11

Cited by 12 publications

(6 citation statements)

References 32 publications

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“…To perform V2G operation, the drivers' responsiveness probability is modelled with reference to the amount of incentive (0–100%), vehicles’ distance from the parking lot (0–3 km), SOC of PEVs, PEV penetration level and drivers' social class (low income, moderate income and high income). The detailed description can be found in [6, 7] and [9].…”

Section: Social and Geographical Aspects Of The Problemmentioning

confidence: 99%

“…Section 4 is concerned with the modelling of geographical and social aspects of PEV fleet [6, 7, 9]. In this section, the drivers' responsiveness probability is modelled with respect to the value of the incentive, PEVs' distances from the parking lot, SOC of PEVs, PEV penetration level, and drivers' social class.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], the optimal value of the power factor of solar parking lots has been investigated after optimally allocating and sizing them in an electrical distribution grid. In [9], the optimal value of incentive for the PEV drivers has been studied to minimise the total cost of the system. In [10], the profit maximisation of a charging station has been studied.…”

Section: Introductionmentioning

confidence: 99%

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Charging management of plug‐in electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force

Rahmani-Andebili

Bonamente

Miller

2020

IET Generation Trans & Dist

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The charging management of plug‐in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle‐to‐grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four‐minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum‐inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results.

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Section: Social and Geographical Aspects Of The Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Charging management of plug‐in electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force

Rahmani-Andebili

Bonamente

Miller

2020

IET Generation Trans & Dist

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“…Mitigating these fluctuations decreases the charging/ discharging rate which increases the lifespan of EV batteries [46]. Moreover, EV batteries should be operated at high SOC levels during V2G operation to optimize their lifespan as described and modeled in [53,54]. Furthermore, it is important to consider the charging plan of EVs which is preset by their owners.…”

Section: Impact Of Ev Batteriesmentioning

confidence: 99%

Voltage fluctuation smoothing in distribution systems with RES considering degradation and charging plan of EV batteries

Ali

Raisz

Mahmoud

2019

Electric Power Systems Research

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Recently, the use of renewable energy sources (RES) and electric vehicles (EVs) has been rapidly increased worldwide. As a result of the highly fluctuating nature of RES, the charging and discharging rates of EVs significantly have to be increased, and so the lifespan of EV batteries decreases. In this paper, an optimization-based method is proposed to smooth voltage fluctuations due to various RES types by optimally controlling the charging and discharging power of EVs and the reactive power of the RES inverters. To extend the lifespan of the EV battery, EV power fluctuations and their minimum preset state of charge (SOC) are considered in the proposed optimization model. For this purpose, a new multi-objective function is formulated, including 1) voltage fluctuations, 2) EV power fluctuations, and 3) the deviation of SOC of EVs from their minimum desired level. The use of the hull moving average (HMA) is proposed to mitigate voltage fluctuations, which eliminates the lag problem of the widely used moving average methods. The gravitational search algorithm (GSA) is utilized to accurately solve the optimization model. The simulation results demonstrate the effectiveness of the proposed method to smooth voltage fluctuations while considering degradation and charging plan of EV batteries.

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“…Moghaddam et al [33] have presented a charging strategy that offers the multiple charging options to the PEVs including AC level II charging, DC fast charging, and battery swapping facilities. In [34], the stochastic presence of an aggregator, owning some parking lots, has been studied in the energy market transactions. In this study, the drivers' behaviour, the hourly capacity of parking lots, the degradation of the PEVs' batteries, and some economic factors such as inflation and interest rates have been considered.…”

Section: Current Counterpart Of Vfgmentioning

confidence: 99%

Vehicle‐for‐grid (VfG): a mobile energy storage in smart grid

Rahmani-Andebili

2019

IET Generation, Transmission & Distribution

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Optimal Incentive Plans for Plug-in Electric Vehicles

Cited by 12 publications

References 32 publications

Charging management of plug‐in electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force

Charging management of plug‐in electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force

Voltage fluctuation smoothing in distribution systems with RES considering degradation and charging plan of EV batteries

Vehicle‐for‐grid (VfG): a mobile energy storage in smart grid

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