Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

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Jian Xu
Yunyan Chang
Xiaoming Sun


In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.

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Special Issue - Deep Learning-Based Advanced Research Trends in Scalable Computing