Optimal load control with the connection of solar energy and wind energy sources to the grid

Authors

  • Dang ThanhTrung EPU
  • Phuc Ky Tran Institute of Energy
  • Quang Vinh Thai Institute of Information Technology

Keywords:

Artificial Neural network, Genetic algorithm, Wind power, Solar power, Control Low Voltage Grid, Particle swarm optimization, mote carlo simulation

Abstract

 This paper uses an artificial neural network to predict the generating capacity of a solar panel, wind turbine, the consumption capacity of the electrical load and the electricity grid price. Then use genetic algorithm and particle swarm optimization to turn on/off of the electrical load to ensure that the nodes voltage and branches current of the low voltage grid are within the allowable range. And ensure that the total cost of selling electricity to the grid from the solar panel and wind turbine minus the total cost of buying electricity from the grid to supply the load is the maximum. This paper also proposes a solution to use smart electric vehicle charging to supply power to the load in case of grid power failure and using monte carlo simulation to calculate the reliability of the grid.

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Published

2022-11-22

How to Cite

ThanhTrung, D., Tran, P. K., & Thai, Q. V. (2022). Optimal load control with the connection of solar energy and wind energy sources to the grid. Measurement, Control, and Automation, 3(2), 37-48. Retrieved from https://mca-journal.org/index.php/mca/article/view/112