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Cluster Computing

, Volume 22, Supplement 4, pp 8835–8845 | Cite as

Coordinated dispatch of the wind-thermal power system by optimizing electric vehicle charging

  • Xizheng ZhangEmail author
  • Liang Zheng
Article
  • 78 Downloads

Abstract

The rapid development of renewable energy poses new challenges to grid operation. Owing to deficiencies in peak regulation capability, wind power cannot be fully connected to the grid. As a controllable load, electric vehicle (EV) charging enables the coordinated operation of EV charging, and the wind/thermal power generation system. In order to determine the coordinated operation mechanism of a power system with large-scale EV charging, a new dynamic multi-objective dispatch (DMOD) model of the power system that includes the economy, pollutant discharge, and abandonment volume was established to optimize the output of thermal power units. Here, we report the two-phase optimization strategy we developed to solve this model. In the first phase of the strategy, EV charging load is determined by optimizing user charging, combined with the time-sharing price of electricity, and the charging protocol. In its second phase, an improved multi-objective evolutionary algorithm (IMOEA) based on a modified particle swarm optimization (PSO) method was proposed to determine the coordinated operation of EV charging, and wind/thermal power generation system. A power system with 10 conventional units, and a grid-connected wind farm was simulated, and the analysis verifies the feasibility of the dispatching model and the effectiveness of the proposed optimization algorithm as a solution.

Keywords

Electric vehicle charging Dynamic economic environmental dispatch Modified particle swarm optimization Wind power 

Notes

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 61673164), the key research project of Education Department of Hunan Province (Grant: 14A032).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Collaborative Innovation Center of Wind Power and Energy ConversionHunan Institute of EngineeringXiangtanChina

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