Akademik Veri Yönetim Sistemi
ENERGY REPORTS, cilt.14, sa.2025, ss.5590-5615, 2025 (SCI-Expanded, Scopus)
The convergence of electric vehicles (EVs) into microgrids (MGs) rapidly alters modern power networks' structure and management frameworks. As EVs gain global prominence, their incorporation into MGs introduces complexities due to their unpredictable charging patterns, mobility, and potential to function as bidirectional energy storage systems using vehicle-to-grid (V2G) technology. Traditional hierarchical control structures, consisting of primary, secondary, and tertiary levels, must be adapted to manage the interactions among EVs, distributed energy resources (DERs), and variable loads. This review paper presents a comprehensive overview of hierarchical control approaches for MGs incorporating EVs, addressing fundamental concepts, recent technology breakthroughs, and increasingly evident obstacles. The investigation methodically examines key components of each control layer, including frequency and voltage stabilization, power sharing, optimal dispatch, and market-oriented coordination. Significant emphasis is placed on enabling technologies, such as artificial intelligence (AI), machine learning (ML), and multi-agent systems (MAS), which facilitate the development of more autonomous, predictive, and decentralized control architectures. The paper also discusses the supplementary function of EVs in conjunction with renewable resources. It outlines the necessity for regulatory standards, communication protocols, and cybersecurity to provide scalable and interoperable MG solutions. Future research directions highlight the adoption of AI-based control strategies, the development of peer-to-peer energy trading mechanisms, the deployment of blockchain-enabled transaction frameworks, and the design of resilience-oriented MG architectures. This review is a valuable resource for researchers, engineers, and policymakers. It synthesizes current information and identifies research gaps, facilitating the optimal utilization of EVs in advancing sustainable, adaptable, and intelligent MG systems.