The impact of large-scale EV charging on the real-time operation of distribution systems: A comprehensive review
Zhe Yu, Chuang Yang, Qin Wang
TL;DR
This paper addresses the challenge of large-scale EV charging on distribution grid real-time operation by first outlining negative impacts on voltage, harmonics, stability, and peak loading. It then surveys management approaches, including smart charging, charging environment optimization, energy coordination, battery management, and ancillary services, complemented by an in-depth look at real-time EV charging management with state estimation and dispatch methods. The work highlights the potential of EVs to provide grid services (e.g., LVRT, frequency regulation, reactive power support) while acknowledging challenges such as battery degradation and computational demands. Practically, the paper guides distribution system operators toward real-time strategies that mitigate risks and harness EV flexibility to improve reliability, efficiency, and resilience of the power system.
Abstract
With the large-scale integration of electric vehicles (EVs) in the distribution grid, the unpredictable nature of EV charging introduces considerable uncertainties to the grid's real-time operations. This can exacerbate load fluctuations, compromise power quality, and pose risks to the grid's stability and security. However, due to their dual role as controllable loads and energy storage devices, EVs have the potential to mitigate these fluctuations, balance the variability of renewable energy sources, and provide ancillary services that support grid stability. By leveraging the bidirectional flow of information and energy in smart grids, the adverse effects of EV charging can be minimized and even converted into beneficial outcomes through effective real-time management strategies. This paper explores the negative impacts of EV charging on the distribution system's real-time operations and outlines methods to transform these challenges into positive contributions. Additionally, it provides an in-depth analysis of the real-time management system for EV charging, focusing on state estimation and management strategies.
