Achieving Net Zero by 2050 demands a rapid expansion of Distributed Energy Resources (DERs), creating a more complex electricity network and introducing new challenges for grid operation, particularly in coordinating service procurement and maintaining system stability. These challenges are exacerbated by limited visibility across transmission-distribution boundaries, particularly for local network controls such as Active Network Management (ANM), market procurement, outage planning, and evolving physical constraints; all of which contribute to uncertainty in DER availability. To overcome these challenges, network operators need enhanced near real-time (half-hourly) visibility of distribution-level activity with analytic capabilities that confidently identify DER availability and evolving system constraints and conflicts. This paper presents a solution to these issues called Fractal Flow, a near-real-time data platform and graph-based analytics engine that will aid in more optimally utilising DERs. Built on a graphical representation of network assets and market procurement information, Fractal Flow localises a power flow analysis problem to facilitate efficient, scalable analysis in near real-time. This paper highlights the current use cases for Fractal Flow within Great Britain’s (GB) electricity system, provides an overview of the innovation, and details a case study demonstrating Fractal Flow’s ability to identify conflicting actions on a portion of the GB network.
Pearson et al. (2026) studied this question.