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Grid integration and management of EVs through machine-to-machine communication

Grid integration and management of EVs through machine-to-machine communication

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The development of vehicle-to-grid (V2G) technology for electric vehicles (EVs) enables prosumers to incorporate other energy storages and participate in largescale bidirectional energy trading. However, the mobility of EVs makes it significantly different from any other conventional storage. For a mass-penetration of EVs, uncoordinated planning and random distribution of V2G may result in power grid instability and power quality degradation. Therefore, a real-time coordinated and automated V2G system in a distributed way is essential for effective energy management. Machine-to-machine (M2M) communication enabling bidirectional information flow between EVs and other power system components is a key element to mitigate challenges associated with their grid integration and manage all parties autonomously. M2M technology assists in improving energy efficiency and reducing any potential risk of instability in power systems. In this chapter, an indepth study of an M2M communication-based coordinated management of EVs is presented. It includes a step-by-step description and practical implementation process of the data logging system, data transmission, and its automatic processing mechanism at the server. Additionally, the scalability issue for EV M2M communication under a 4G Long Term Evolution transceiver base station is extensively examined. Various numerical simulations with and without radio network scheduling are also presented to provide a detailed understanding of this scalability issue, taken into consideration communication delays and blocking rate.

Chapter Contents:

  • List of abbreviations
  • 2.1 Introduction
  • 2.2 M2M in distributed energy management systems
  • 2.3 M2M communication for EVs
  • 2.3.1 M2M communication architecture (3GPP)
  • 2.4 Electric vehicle data logging systems
  • 2.4.1 Data logging system
  • 2.4.2 Hardware description
  • 2.4.2.1 Interfacing GPS with Raspberry Pi
  • 2.4.3 DLS operation
  • 2.5 Scalability of electric vehicles
  • 2.5.1 Radio resource and IP connection of LTE
  • 2.5.2 Radio access of M2M
  • 2.5.3 LTE user-plane protocol
  • 2.5.4 Analytical model
  • 2.5.5 Simulation and performance evaluation
  • 2.6 M2M communication with scheduling
  • 2.6.1 LTE scheduling
  • 2.6.2 LTE popular scheduling algorithms
  • 2.6.2.1 Proportional fair scheduling
  • 2.6.2.2 Modified largest weighted delay first scheduling
  • 2.6.2.3 Exponential scheduling
  • 2.6.3 Performance evaluation
  • 2.7 Conclusion
  • References

Inspec keywords: vehicle-to-grid; transceivers; radio networks; scheduling; energy conservation; machine-to-machine communication; power supply quality; energy storage; energy management systems; electric vehicles; power system stability; Long Term Evolution

Other keywords: machine-to-machine communication; automated V2G system; communication delays; vehicle-to-grid technology; energy efficiency; power grid instability; energy management; data transmission; M2M communication-based coordinated management; automatic processing mechanism; bidirectional information flow; 4G Long Term Evolution transceiver; EV M2M communication; power system components; EV grid integration and management; energy storages; data logging system; power quality degradation; EV mass-penetration; radio network scheduling; bidirectional energy trading; numerical simulations; blocking rate; electric vehicles

Subjects: Transportation; Mobile radio systems; Power system control; Other topics in energy storage; Energy conservation; Other energy storage; Power system management, operation and economics; Computer communications; Power supply quality and harmonics; General transportation (energy utilisation)

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