Heuristic methods for optimal electric vehicle charging scheduling in smart grid

Xu, Guangyang; Bai, Lihui

doi:10.1504/ijal.2013.057449

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…Papers dealing with individual subsystems can be found. Thus, papers [4,5] show the analysis of terminal hardware and software for the system for remote control of meters, while papers [6,7] show the analysis of hardware and software for the system for remote control of fiscal cash registers, while papers [8][9][10][11][12][13] provide an analysis of a system for remote control of EV chargers. Paper [14] provides an overview of works dealing with systems and terminals for remote control of charging stations for electric vehicles powered by solar power plants.…”

Section: Related Workmentioning

confidence: 99%

“…The process transitions through various states as it is being executed. The process of making decisions in demand side management [31] and the process of controlling electric car chargers optimally to reduce network load [8], employing various tactics [9][10][11][12][13] depending on the tariff, are examples of more complex processes. The Initialization of global variables, Configuration of other components, Configuration of the microcontroller, and Recovering from a power outage subprograms are all included in the group of subprograms for starting the terminal.…”

Section: Software Of the Terminalmentioning

confidence: 99%

Section: Software Of the Terminalmentioning

confidence: 99%

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Model-Driven Developed Terminal for Remote Control of Charging Station for Electric Vehicles Powered by Renewable Energy

2023

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A terminal for remote control of charging stations for electric vehicles (EV) powered by renewable energy has been presented in this paper. This terminal enables remote control of EV chargers, smart batteries, smart electricity meters, fiscal cash registers (FCR), as well as remote control of renewable energy sources and other devices within the station. This terminal also makes charging stations more accessible to electric vehicles users, to electricity distribution companies, to electricity suppliers, to tax administrations, and finally to users and owners of charging stations. Therefore, communication and control with all these devices and systems is integrated in one device. Realization of hardware and software of such a terminal has also been described in this paper. The net result of development and commercialization of terminals would encourage an increase in the use of electric vehicles powered by energy from renewable sources, which would cause a decrease in the level of air pollution and all negative effects it causes in the future. Different categories of this device are considered. Moreover, although it is a device with embedded software, a very advanced method was used, that is, a model-driven development method, which enables fast and more efficient development and maintenance of the device. The results of the application of this method to the terminal for remote control of fiscal cash registers are provided. They were compared with the results of the development of the terminal for remote control of smart meters without applying this method. A simulation of the development of the terminal for remote control of the station is also provided. The presented method can be used in the future for faster and better-quality development of embedded software.

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Section: Related Workmentioning

confidence: 99%

Section: Software Of the Terminalmentioning

confidence: 99%

Section: Software Of the Terminalmentioning

confidence: 99%

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Model-Driven Developed Terminal for Remote Control of Charging Station for Electric Vehicles Powered by Renewable Energy

2023

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“…In the literature, Xu and Bai [77] introduced the fixed setup cost to help reduce the percentage of non-consecutive PHEV charging in a public charging station. Through carefully selected fixed setup cost, the percentage of non-consecutive charging can be reduced by approximately 80%.…”

Section: Development Of Mip Modelsmentioning

confidence: 99%

“…LT i denotes its lower limit of energy consumption once it is on. Thus, we replace the TSTAT demand constraints (57) to (59) with (77).…”

Section: Development Of Mip Modelsmentioning

confidence: 99%

Optimization models and algorithms for demand response in smart grid.

Xu¹

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Review of Different Optimization Techniques for Charging and Market Participation of Electric Vehicles

Gržanić

Canuder

Pandžić

et al. 2022

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Heuristic methods for optimal electric vehicle charging scheduling in smart grid

Cited by 5 publications

References 30 publications

Model-Driven Developed Terminal for Remote Control of Charging Station for Electric Vehicles Powered by Renewable Energy

Model-Driven Developed Terminal for Remote Control of Charging Station for Electric Vehicles Powered by Renewable Energy

Optimization models and algorithms for demand response in smart grid.

Review of Different Optimization Techniques for Charging and Market Participation of Electric Vehicles

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