电气工程学报 ›› 2017, Vol. 12 ›› Issue (3): 48-52.doi: 10.11985/2017.03.007

• 北方工业大学专刊(二) • 上一篇    

考虑电动汽车的配电网无功优化

赵彦锦1,2,孟庆海1,2,陈鹏1,2,王进己1,2   

  1. 1. 北方工业大学电气与控制工程学院 北京 100041
    2. 北京电动车辆协同创新中心 北京 100041
  • 收稿日期:2016-05-16 出版日期:2017-03-25 发布日期:2020-04-11
  • 作者简介:赵彦锦,男 1990年生,硕士研究生,研究方向为配电网运行优化。|孟庆海,男 1971年生,研究员,博士,研究方向为电气安全,电力系统可靠性。

Reactive Power Optimization for Distribution Network with the Electric Vehicle

Yanjin Zhao1,2,Qinghai Meng1,2,Peng Chen1,2,Jinji Wang1,2   

  1. 1. North China University of Technology Beijing 100041 China
    2. Collaborative Innovation Center of Electric Vehicles in Beijing Beijing 100041 China
  • Received:2016-05-16 Online:2017-03-25 Published:2020-04-11

摘要:

大规模电动汽车充电接入电网后,由于其充电时空的不确定性给配电网无功优化带来了新的挑战。本文研究了包含电动汽车的配电网无功优化模型,将配电网每个节点的充电功率、机端电压、调压器分接头档位及无功补偿装置的补偿容量作为控制变量,以最小化配电网的网络损耗为目标函数。首先,模拟仿真了电动汽车无序充电负荷;其次,建立了最小化配电网网络损耗的无功优化的数学模型;最后,基于配电网33节点模型,采用Matlab验证了优化模型的有效性。结果表明,该优化模型可以有效地降低配电网网损、改善电压质量。

关键词: 电动汽车, 配电网, 无功优化, 蒙特卡洛

Abstract:

After large-scale electric vehicle charging connected to the grid, due to the uncertainty of the charging time and space, new challenges to the reactive power optimization in the distribution network. Reactive power optimization model of distribution network including electric vehicle is studied in this paper, the power of chargingof each node, the terminal voltage, the position of the tap position and the compensation capacity of the reactive power compensation device are used as the control variables, the objective function is to minimize the network loss of distribution network. First, simulation of electric vehicle free charging load; Secondly, the paper established a mathematical model to minimize the loss of distribution network; Finally, based on the 33 node model of distribution network, using Matlab verifyoptimization model effectiveness. The results show that the optimization model can effectively reduce distribution network losses and improve voltage quality.

Key words: Electric vehicle, distribution network, reactive power optimization, Monte Carlo

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