Power flow tracing (PFT) methods algorithmically reconstruct how electricity generators supply specific loads and contribute to network losses, enabling physically grounded attribution of electricity flows across a grid. Despite nearly three decades of research, the field lacks a unified conceptual framework that integrates academic and industry perspectives. In this paper, we address this gap by conducting a multivocal literature review (MLR) covering 52 academic and industry sources published between 2019 and 2025, and developing a taxonomy of PFT methods structured along six dimensions and 20 characteristics: input, output, tracing approach, application area, topology model, and level of analysis. Our analysis reveals that linear-equation-based methods embodying the proportional sharing principle dominate both academic and practitioner contexts, and that emissions attribution and renewable energy certification have emerged as the primary application areas, primarily driven by tightening sustainability reporting requirements. While PFT methodologies themselves exhibit considerable maturity, we find that limited data availability, granularity, and quality represent the central barrier to broader practical adoption. We discuss how digital technologies can support the measuring, reporting, and verification of electricity data to overcome these barriers, and propose a research agenda from a data perspective. Our taxonomy supports policymakers and grid operators in selecting suitable PFT methods for regulatory, technical, and operational contexts.
Ströher et al. (2026) studied this question.