Optimal charging scheduling of plug-in electric vehicles for maximizing penetration within a workplace car park

Mehta, Rahul; Srinivasan, Dipti; Trivedi, Anupam

doi:10.1109/cec.2016.7744251

Cited by 29 publications

(11 citation statements)

References 21 publications

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“…Smart charging [10], which allows the control of charging processes in a coordinated way, is often seen as an important step towards a successful grid integration of EVs and a profitable operation of public EV charging stations. Different approaches towards increasing the grid stability [11,12] and the profit of EV charging station operators [13][14][15] are proposed in the literature. Smart charging could be also applied by individual users in order to reduce their charging costs [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Limmer

2019

Energies

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Time-varying pricing is seen as an appropriate means for unlocking the potential flexibility from electric vehicle users. This in turn facilitates the future integration of electric vehicles and renewable energy resources into the power grid. The most complex form of time-varying pricing is dynamic pricing. Its application to electric vehicle charging is receiving growing attention and an increasing number of different approaches can be found in the literature. This work aims at providing an overview and a categorization of the existing work in this growing field of research. Furthermore, user studies and the modeling of user preferences via utility functions are discussed.

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Section: Introductionmentioning

confidence: 99%

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Limmer

2019

Energies

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“…The results showed the lowering in the power fluctuation level and in the overall peak demand in the grid system. Another paper [36] proposed an optimal charging scheduling strategy, based on an integrated grid-to-vehicle (G2V) and vehicle-to-grid (V2G), within a workplace car park. Authors modeled the EVs driving pattern and based on it designed a fuzzy inference system to model the EVs energy requirement.…”

Section: Charging Previsionmentioning

confidence: 99%

Fleet Transition from Combustion to Electric Vehicles: A Case Study in a Portuguese Business Campus

et al. 2020

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This paper aims to contribute to the urgent reflection as a society about environmental protection, in the ultimate challenge that is the sustainable use of energy resources. Since Portugal is at an early stage of market development internally, governmental and local stimulation policies play a central role and are a key element in the successful diffusion of Electric Mobility. The study will focus on the transition of a company car fleet, which currently consists of combustion vehicles, to electric vehicles. With this change it becomes necessary to understand how the electrical installation will be affected due to the installation of charging stations, allowing the company to have some autonomy from the public grid. The various changes resulting from the installation consumption profile will be studied and compared. The state of the art, the level of maturity and where the development of electric mobility in Portugal is heading will also be appreciated.

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“…A key factor in this analysis is an accurate representation of customers and their charging requirements. In the majority of prior literature (for example [1,2], see also [3,4]), customers were modeled passively via their charging process with an arrival and departure time of the car plus an energy requirement. These values are typically described as a Gaussian probability distribution with a defined mean and variance.…”

Section: Introductionmentioning

confidence: 99%

Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems

et al. 2019

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The development of efficient electric vehicle (EV) charging infrastructure requires a modeling of customer behavior at an appropriate level of detail. Since only limited information about real customers is available, most simulation approaches employ a stochastic approach by combining known or estimated customer features with random variations. A typical example is to model EV charging customers by an arrival and a targeted departure time, plus the requested amount of energy or increased state of charge (SoC), where values are drawn from normal (Gaussian) distributions with mean and variance values derived from user studies of obviously limited sample size. In this work, we compare this basic approach with a more detailed customer model employing a multi-agent simulation (MAS) framework in order to investigate how a customer behavior that responds to external factors (like weather) or historical data (like satisfaction in past charging sessions) impacts the essential key performance indicators of the charging system. Our findings show that small changes in the way customers are modeled can lead to quantitative and qualitative differences in the simulated performance of EV charging systems.

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Optimal charging scheduling of plug-in electric vehicles for maximizing penetration within a workplace car park

Cited by 29 publications

References 21 publications

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Fleet Transition from Combustion to Electric Vehicles: A Case Study in a Portuguese Business Campus

Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems

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