A novel optimization method for high-penetration electric vehicles integration to electrical distribution network

Xiaoyan, Ji and Tian, Wu and Can, Wang and Yuli, Wang (2022) A novel optimization method for high-penetration electric vehicles integration to electrical distribution network. Frontiers in Energy Research, 10. ISSN 2296-598X

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Abstract

With the implementation of China’s “double carbon” strategy, the usage scale of electric vehicles has grown rapidly. Therefore, how to maintain the ideal voltage and energy curve in the distribution network with high penetration of electric vehicles is a challenging problem. In this paper, the random behaviors of fast charging, ordinary charging and discharging modes of electric vehicles have been analyzed and the mathematical formulation of the algorithm has been presented. Then, the strategy of coordinated charging-discharging stack of electric vehicles is proposed. The improved particle swarm optimization algorithm based on mixed real number and binary vector is used to solve the optimization model. The results of several case studies have also been presented in this paper to show that optimum capacitor switching and transformer tap adjustment solutions can be found to minimize the total operation cost including energy consumption, power quality and reactive power compensation equipment action cost. The paper demonstrates that the impact of high-penetration electric vehicles on the energy and voltage control of the distribution network has been solved. The proposed EV coordinated stacking method can make electric vehicles charge and discharge in an orderly queue, and ensure that the line flow does not exceed the limit. Through the proposed control strategy, the voltage curve is obviously improved, and the cost of the distribution system with large-scale EVs can be effectively reduced.

Item Type: Article
Subjects: Digital Open Archives > Energy
Depositing User: Unnamed user with email support@digiopenarchives.com
Date Deposited: 11 May 2023 07:09
Last Modified: 15 Oct 2024 10:28
URI: http://geographical.openuniversityarchive.com/id/eprint/1147

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