​​​​​​​International Journal of Modern Science and Technology, Vol. 2, Special Issue 1, 2017, Pages 71-75. 

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​Prioritized Energy Optimal Management for Home-to-Home using Various Green Energy Resources

S. Sivasakthi, R. Pazhanimurugan
Department of Electrical and Electronics Engineering, Arasu Engineering College, Kumbakonam – 612 501. India.

​*Corresponding author’s e-mail: sakthibharathi276@gmail.com; rpmuruganmail@gmail.com

​Abstract
This paper proposed a prioritized energy optimal management for home-to-home in smart grid. As this proposed mechanism configures the prioritized optimal energy, if there is a surplus of energy from the energy provider of home, the energy broker in smart grid distributes the optimal energy to the requesting user for the purpose of maximizing the user’s payoff. We propose a various green energy sources method that can improve performance. This method collects data from green power storage system and smart meters by internet. In order to deploy power in smart grids, the control system collects data and analyze it by cloud computing. The green power storage system can predict the power status and control the system to work. Smart meters will collect user’s data and send it to cloud computing servers, and then it will predict the power status of green storage system and provide extra energy to smart grid. If the green power storage system cannot supply the users enough energy, it can extend the number or variety of green energy sources to increase the provided energy.

​​Keywords: Smart grid; Green energy; Home to Home system; Prioritized energy usage.

​References

  1. Chen C, S. Duan. Optimal Integration of Plug-In Hybrid Electric Vehicles in Microgrids. IEEE Trans Industrial Informatics. 2014;10:1917-1927.
  2. Ferreira JC, Monteiro V, Afonso JL. Vehicle-to-Anything Application (V2Anything App) for Electric Vehicles. IEEE Trans Industrial Informatics. 2014;10:1927-1938.
  3. Darabi Z, Ferdowsi M. An Event-Based Simulation Framework to Examine the Response of Power Grid to the Charging Demand of PlugIn Hybrid Electric Vehicles. IEEE Trans Industrial Informatics. 2014;10:313-322.
  4. Kabir MN, Mishra Y, Ledwich G, Dong ZY, Wong KP.Coordinated Control of Grid-Connected Photovoltaic Reactive Power and Battery Energy Storage Systems to Improve the Voltage Profile of a Residential Distribution Feeder. IEEE Trans Industrial Informatics. 2014;10:967-977.
  5. Sun J, Palade V, Wu XJ, Fang W, Wang Z. Solving the Power Economic Dispatch Problem with Generator Constraints by Random Drift Particle Swarm Optimization. IEEE Trans. Industrial Informatics, 2014;10:222-232.
  6. Golshanavaz S, Afsharnia S, Aminifar F. Smart Distribution Grid: Optimal Day-Ahead Scheduling With Reconfigurable Topology. IEEE Trans Smart Grid. 2014;5:2402-2411.
  7. Tani V, Camara MB, Dakyo B, Azzouz V. DC/DC and DC/AC converters control for hybrid electric vehicles energy management ultracapacitors and fuel cell. IEEE Trans Industrial Informatics. 2013;9:686-696.
  8. Abdul Jaleel J, Nazar A, Omega AR. Simulation on Maximum Power Point Tracking (MPPT) of the Photovoltaic Module using LabVIEW. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2012;1(3):190-199. 
  9. Depuru S, Wang L, Devabhaktuni V. Support vector machine based data classification for detection of electricity theft. Proceedings of the in IEEE/PES Power Systems Conference and Exposition, 2011. pp. 1–8.

International Journal of Modern Science and Technology

INDEXED IN 

ISSN 2456-0235