A Review of Energy Storage Systems for Facilitating Large-Scale Ev Charger Integration in Electric Power Grid

Abstract

The swift increase in electric vehicle (EV) into modern power grids presents both significant opportunities and challenges, particularly in maintaining power quality (PQ) and managing peak loads. This review synthesizes current research, providing a comprehensive analysis of the pivotal role of energy storage systems (ESS) in enabling large-scale EV charger integration while addressing critical PQ issues. A key contribution is the comparative evaluation of various ESS typologies-battery ESS (BESS), hybrid ESS (HESS), and distributed ESS (DESS)-each offering distinct advantages in mitigating PQ challenges such as harmonic distortion, voltage regulation, and peak demand control. Ensuring compliance with IEEE-519 standards is emphasized as vital for maintaining grid reliability and high PQ standards. This review paper further examines the diverse impacts of plug-in electric vehicles (PEVs) on power grids, including their charging and storage characteristics, which influence grid stability and efficiency. It highlights the transformative potential of vehicle-to-grid (V2G) technology, which facilitates bidirectional power flow to support grid stabilization, energy balancing, and ancillary services. Additionally, it addresses the mitigation of harmonic distortion from PEV charging, preserving transformer performance and lifespan, and explores strategies to manage large-scale PEV integration through predictive and adaptive control techniques. This study introduces innovative approaches to improving grid recovery following disturbances and evaluates the synergistic integration of renewable energy sources with PEVs to foster sustainable energy systems. Models for PEV interaction with microgrids are also discussed, emphasizing their role in enhancing energy resilience and grid flexibility. This paper underscores the critical role of advanced energy management strategies (EMS) in optimizing EV-grid integration and improving overall system efficiency. These strategies include rule-based EMSs employing fixed rules, fuzzy logic, and wavelet transforms; optimization-based EMSs utilizing techniques such as dynamic programming, genetic algorithms, model predictive control, and particle swarm optimization; and intelligent EMSs leveraging neural networks and reinforcement learning for adaptive and predictive control. By outlining innovative solutions and highlighting the importance of strategic collaboration among utilities, policymakers, researchers, and technology developers, this review provides a comprehensive roadmap for overcoming the technical, economic, and regulatory challenges associated with EV charger integration, laying the groundwork for a reliable, efficient, and sustainable energy future.