The parametric analysis of the electric vehicles and vehicle to grid system's role in flattening the power demand


SUSTAINABLE ENERGY GRIDS & NETWORKS, vol.30, 2022 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30
  • Publication Date: 2022
  • Doi Number: 10.1016/j.segan.2022.100605
  • Keywords: Electric vehicle, Vehicle to grid (V2G), Power demand, Smart grid, Monte Carlo simulation, Peak demand, Valley demand, ENERGY-STORAGE, TECHNOLOGY, MANAGEMENT, REDUCTION, BATTERIES, IMPACT


Although the deployment of electric vehicles (EVs) increases the power demand, implementing the vehicle to grid technology (V2G) can decrease the power issues and improve the efficiency of the network. A transition to the V2G system enables EVs to flatten the load profile by shaving the peak demand and filling the valley demand by utilizing the unused/extra stored power in batteries to support the grid. Many authors have focused on shaving the peak demand with different methods like energy storage system (ESS) and demand-side management (DSM) and utilized various algorithms to assess the impacts of EVs and V2G system on shaving the peak demand. In most of these papers, only limited aspects of the implementation of V2G and its impacts on peak demand have been analyzed. In this paper, we propose a novel methodology to comprehensively evaluate the role of the EVs and V2G on shaving the peak demand and filling the valley demand under different parameters such as penetration level of EVs and V2G, charging mode, charging location, and schedule of charging. Monte Carlo simulation is utilized to analyze the influences of parameters on the power demand profile. The results indicate that un-controlled charging intensifies the peak demand up to 5% more than off-peak scenarios that negatively affect the grid's reliability. Among analyzed cases, integrating the EVs and V2G system under off-peak charging has better consequences in shaving the peak and filling the valley demand. The off-peak mode can level the load curve where the peak demand is decreased around 2%, and the valley demand is increased around 3%. Based on the outputs of the simulation, encouraging the EVs' owners to charge their electric batteries at late night or early morning would be the best policy to improve the power grid's performance. Charging at home would be a better option for leveling the load profile among all charging stations. Moreover, analysis indicates that there is a need for policies to regulate the schedule of charging at public stations. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.