Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming

@article{Xie2016FairES,
  title={Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming},
  author={Shengli Xie and Weifeng Zhong and Kan Xie and Rong Yu and Yan Zhang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2016},
  volume={27},
  pages={1697-1707}
}
Research on the smart grid is being given enormous supports worldwide due to its great significance in solving environmental and energy crises. Electric vehicles (EVs), which are powered by clean energy, are adopted increasingly year by year. It is predictable that the huge charge load caused by high EV penetration will have a considerable impact on the reliability of the smart grid. Therefore, fair energy scheduling for EV charge and discharge is proposed in this paper. By using the vehicle-to… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 34 CITATIONS

References

Publications referenced by this paper.
SHOWING 1-10 OF 54 REFERENCES

Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data

  • IEEE Transactions on Neural Networks and Learning Systems
  • 2015
VIEW 1 EXCERPT

Optimal Coordination of G2V and V2G to Support Power Grids With High Penetration of Renewable Energy

  • IEEE Transactions on Transportation Electrification
  • 2015
VIEW 2 EXCERPTS

Similar Papers

Loading similar papers…