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Conservation voltage reduction (CVR) is a potentially effective and efficient technique for inertia synthesis and frequency support in modern grids comprising power electronics (PE)-based components, aiming to improve dynamic stability. However, due to the complexities of PE-based grids, implementing the CVR methods cannot be performed using traditional techniques as in conventional power systems. Further, quantifying the CVR impacts in modern grids, while focusing on dynamic time scales, is critical, consequently making the traditional methods deficient. This is an important issue as CVR utilization/quantification depends on grid conditions and CVR applications. Considering these concerns, this work offers a thorough analysis of CVR applications, implementation, and quantification strategies, including data-driven AI-based methods in PE-based modern grids. To assess the CVR applications from a new perspective, aiming to choose the proper implementation and quantification techniques, they are divided into categories depending on various time scales. CVR implementation methods are categorized into techniques applied to PE-based grids and islanded microgrids (MGs) where different control systems are adopted. Additionally, to address the evaluation issues in modern grids, CVR quantification techniques, including machine learning- and deep learning-based techniques and online perturbation-based methods are evaluated and divided based on the CVR application. Concerns with the further utilizing and measuring of CVR impacts in modern power systems are discussed in the future trends section, where new research areas are suggested.