Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

Bharatiraja, C.; Padmanaban, Sanjeevikumar; Siano, Pierluigi; Krishnamoorthy, Ramesh; Selvaraj, Raghu

doi:10.3390/en10030377

Cited by 51 publications

(21 citation statements)

References 23 publications

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“…The ripple components of the generator speed and the output power are also eliminated and the parameters are subjected within the rated value. Figure 7 shows the pitch angle control strategy [26][27][28][29][30][31][32][33].…”

Section: Resultsmentioning

confidence: 99%

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

2017

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Abstract:In this paper, a novel co-ordinated hybrid maximum power point tracking (MPPT)-pitch angle based on a radial basis function network (RBFN) is proposed for a variable speed variable pitch wind turbine. The proposed controller is used to maximise output power when the wind speed is low and optimise the power when the wind speed is high. The proposed controller provides robustness to the nonlinear characteristic of wind speed. It uses wind speed, generator speed, and generator power as input variables and utilises the duty cycle and the reference pitch angle as the output control variables. The duty cycle is used to control the converter so as to maximise the power output and the reference pitch angle is used to control the generator speed in order to control the generator output power in the above rated wind speed region. The effectiveness of the proposed controller was verified using MATLAB/Simulink software.

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Section: Resultsmentioning

confidence: 99%

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

2017

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“…Finally, the minimum of dimensions horizontal 27 µm 2 and vertical 32 µm 2 is taken as the dimensions.The minimum of these is, 27 µm 2 . Its co-ordinates are (3,8) and (3,1). The area of the merged modules {n 4 , n 3 } is fixed as (3, 8).…”

Section: Example For Determining Optimal Dimensions Of Modulesmentioning

confidence: 99%

“…Design of low power integrated circuits (IC) has become a challenging problem due to the continuous technology scaling. In the design of chips for modern applications like solar [1,2], motor control [3,4] and smart energy systems [5], power consumption plays a significant role. Comparatively, the contribution of dynamic power due to the frequent switching action of more number of transistors is more pronounced in these ICs to static power dissipation.…”

Section: Introductionmentioning

confidence: 99%

Module Based Floorplanning Methodology to Satisfy Voltage Island and Fixed Outline Constraints

et al. 2018

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Multiple supply voltage is the most prevalent method for low power reduction in the design of modern Integrated circuits. Floorplanning process in this design performs positioning of functional blocks in the layout satisfying both fixed outline and voltage island constraints. The floorplans while satisfying these two significant constraints causes significant rise in wirelength and congestion. In this paper, a congestion and wirelength aware floorplanning algorithm is proposed which allows effective placement of functional blocks in the layout to satisfying fixed outline and voltage island constraints simultaneously. To perform voltage island floorplanning, the proposed algorithm uses Skewed binary tree representation scheme to operate the functional blocks in its predefined voltage level. The proposed methodology determines the feasible dimensions of the functional blocks in the representation which aids the placement process for the reduction of congestion and wirelength. With these optimal dimensions of the functional blocks, floorplanning is also performed for the layouts of aspect 1:1, 2:1, and 3:1, to evaluate the ability of proposed algorithm for satisfying the fixed outline constraint. The proposed methodology is implemented in the layout of InternationalWorkshop on Logic and Synthesis (IWLS) benchmarks circuits for experimental purpose. The resulting floorplans were iteratively optimized for optimal reduction of wirelength and congestion. Experimental results show that the proposed methodology outperforms existing state-of-the-art approaches in wirelength reduction by about 18.65% and in congestion reduction by around 63%, while delivering the 30.35% power consumption.

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“…In this model, they assume that the charging process only starts when the EVs have a low battery and the communication between the stations and the EVs performed directly using the vehicular ad-hoc network. In [8], the authors propose a model to help EV drivers locate the nearest charging station, they implement an interactive application developed via SQL and PHP platforms to assign EVs their charging locations. In order to make a vision of the smart city via dispersed detection devices, the Internet of Things (IoT), the authors in [9] discusses a data base, big data, based on the history of demand for energy, the location of EVs, and the time of connection.…”

Section: Introductionmentioning

confidence: 99%

Application Design Aiming to Minimize Drivers’ Trip Duration through Intermediate Charging at Public Station Deployed in Smart Cities

El-Fedany

Kiouach

Alaoui

2019

WEVJ

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Today, smart cities are turning to electric transport, carpooling and zero emission zones. The growing number of electric vehicles on the roads makes it increasingly necessary to have a public charging infrastructure. On the other hand, the main limitations of electric vehicles are the limited range of their batteries and their relatively long charging times. To avoid having problems to recharge, electric vehicle drivers must plan their journeys more thoroughly than others. At the goal of optimizing trip time, drivers need to automate their travel plans based on a smart charging solution, which will require the development of new Vehicle-to-Grid applications that will allow at the charging stations to dynamically interact with the vehicles. In this paper, we propose an architecture based on an algorithm allowing the management of charging plans for electric vehicles traveling on the road to their destination, in order to minimize the duration of the drivers’ journey including waiting and charging times. The decision taken by the algorithm based on the exploration of the data of each public supply station according to its location, number of vehicles in the queue, number of charging sockets, and rates of service.

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Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

Cited by 51 publications

References 23 publications

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

Module Based Floorplanning Methodology to Satisfy Voltage Island and Fixed Outline Constraints

Application Design Aiming to Minimize Drivers’ Trip Duration through Intermediate Charging at Public Station Deployed in Smart Cities

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