• Title of article

    A scenario of vehicle-to-grid implementation and its double-layer optimal charging strategy for minimizing load variance within regional smart grids

  • Author/Authors

    Jian، نويسنده , , Linni and Zhu، نويسنده , , Xinyu and Shao، نويسنده , , Ziyun and Niu، نويسنده , , Shuangxia and Chan، نويسنده , , C.C.، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2014
  • Pages
    10
  • From page
    508
  • To page
    517
  • Abstract
    As an emerging new electrical load, plug-in electric vehicles (PEVs)’ impact on the power grid has drawn increasing attention worldwide. An optimal scenario is that by digging the potential of PEVs as a moveable energy storage device, they may not harm the power grid by, for example, triggering extreme surges in demand at rush hours, conversely, the large-scale penetration of PEVs could benefit the grid through flattening the power load curve, hence, increase the stability, security and operating economy of the grid. This has become a hot issue which is known as vehicle-to-grid (V2G) technology within the framework of smart grid. In this paper, a scenario of V2G implementation within regional smart grids is discussed. Then, the problem is mathematically formulated. It is essentially an optimization problem, and the objective is to minimize the overall load variance. With the increase of the scale of PEVs and charging posts involved, the computational complexity will become tremendously high. Therefore, a double-layer optimal charging (DLOC) strategy is proposed to solve this problem. The comparative study demonstrates that the proposed DLOC algorithm can effectively solve the problem of tremendously high computational complexity arising from the large-scaled PEVs and charging posts involved.
  • Keywords
    electric vehicle , Charging strategy , Vehicle-to-grid , Smart Grid , Load variance , Regional grid
  • Journal title
    Energy Conversion and Management
  • Serial Year
    2014
  • Journal title
    Energy Conversion and Management
  • Record number

    2337423