Project Summary
Fractal Flow (FF) provides a core digital capability for the UK electricity system, creating a shared, near-real-time digital twin to improve visibility and coordination across the transmission-distribution boundary. As Distributed Energy Resources (DERs) grow, FF gives DSOs and NESO trusted insight into assets, constraints, flexibility services, and emerging conflicts, strengthening control room decision-making, coordination, and situational awareness. By improving DER utilisation and reducing unnecessary curtailment and balancing actions, FF lowers system operating costs, supports reducing consumer energy bills, increases access to revenues for flexibility providers, and drives low-carbon outcomes. FF establishes foundational infrastructure for resilient, efficient, and data-driven network operations.
Innovation Justification
How does your Project demonstrate novel and ambitious innovation in the energy networks?
Innovation Justification
Fractal Flow (FF) addresses SIF Round 5 Challenge 5: Improve control room capabilities and coordination, Theme 2: Enhancing control room operations. It delivers a first-of-its-kind operational-timescale system that links network data, forecasts, and network actions to detect conflicts, assess Balancing Mechanism (BM) and flexibility procurement effectiveness, and coordinate decisions in DSO/NESO control rooms.
FF meets the challenge theme by:
Enabling near-real-time (half-hourly), distribution-level, network and asset activity visibility across the transmission-distribution boundary, including constraints, interactions with active network management (ANM) systems, and primacy rules.
Providing data-driven analytics and combined forecasting to improve situational awareness and support more confident, effective BM and flexibility procurement.
Improving control room capabilities and transmission-distribution coordination through a digital twin that helps manage growing grid complexity.
SIF Learning
Discovery (NPg, NESO, Frazer-Nash) identified an industry-wide gap in near-real-time distribution-level visibility and cross-network situational awareness. Alpha (NPg, NESO, Frazer-Nash, SP ENW, ElectraLink, OakTree) demonstrated how a knowledge graph of network assets and actions can contextualise power-flow as it evolves half-hourly, enabling evaluations of flexibility/BM effectiveness and identifying ANM/BM conflicts up to 24-hours ahead. Engagement with control room and DSO teams (including SSEN-D and UKPN) confirmed FF can provide operators with necessary context and shared understanding across organisational boundaries.
Open Working
FF has openly worked with Data Sharing Infrastructure, Operational Data Sharing Technical Working Group (TWG), and Primacy TWG. FF is a valuable use case for these initiatives and leverages their outputs. FF continues engaging with Volta, NESO's AI control room decision-making tool, and will provide distribution-level insights at BAU. Beta will use Artificial Forecasting's forecasts and data sharing lessons from Power Wales Renewably (PWR).
Why SIF Funding?
FF requires extensive cross-network collaboration to de-risk innovations, joint development, data integration, and workflow alignment across NESO and DSOs, before operational deployment. These activities fall outside BAU RIIO price-control mechanisms, making SIF appropriate for the required collaboration and scale of progress.
Innovation and State-of-the-Art
FF's novel capabilities are built on:
Knowledge graph: Defines relationships between network assets and actions helping to contextualise how network actions impact power flow.
Forecasts: Combines DNO and NESO forecasts into a unified view up to 24-hours ahead of the settlement period.
Probabilistic power-flow: Predicts possible power-flow changes through the network 24-hours ahead of the settlement period.
Primacy rules and ANMs: Applies primacy and ANM logic within knowledge graph relationships to detect conflicts.
Optimal dispatch: Produces feasible dispatch/procurement options based on network constraints and near-real-time analytics.
Current NESO and DSO control room systems lack these capabilities. Existing tools address isolated aspects of DER visibility, but none address full cross-network coordination nor form a cohesive solution. FF achieves this through unprecedented coordination between NESO and DSOs, not yet seen in the UK.
Other innovations have narrower focus:
Megawatt Dispatch (MWD) can turn DERs down to zero but lacks cross-network visibility and near-real-time situational awareness.
DSO specific tools don't offer cross-network coordination.
Digital twins like Powering Wales Renewably target long-term planning rather than near-real-time operational decision-making.
Readiness Transitions
TRL 4→7: Beta will demonstrate FF in NPg, SP ENW, and NESO control rooms.
IRL 3→5: Integration of FF into DSO and NESO environments.
CRL 4→5: Defined product, validated workflows, and clear deployment pathway.
Appropriate Scale and Counterfactual
Beta will validate FF under operational conditions for three networks (NPg, SP ENW, and NESO), enabling future expansion to SSEN-D, UKPN, NGED, and SPEN. Alternatives such as DSO-centric tools, MWD, or strategic digital twins, cannot deliver the whole-system cross-network situational awareness required for future grid operations. Without FF, operators face increasing uncertainty and operational risk as DER penetration grows, and consumers face higher bills through rising BM expenditure.
Impacts and Benefits
Headline
Forecasts indicate Fractal Flow (FF) will deliver \>£689m Net Present Value (NPV) and indirect CO2 reductions of \>36.5ktonnes between 2026-2045, assuming a phased rollout from 2030-2035 under the 'Holistic Transition' Future Energy Scenario (FES) 2025. This demonstrates high probability of significant whole-system benefits that will pass to consumers through Ofgem's RIIO Framework. FF will:
Reduce network operating costs through better cross-network coordination and situational awareness,
Indirectly lower CO2 emissions by minimising renewable curtailment,
Provide consumer energy bill savings through whole-system savings pass-through,
Improve revenue access for network service users through clearer GB-wide operational signals.
Probabilistic Monte Carlo (MC) analysis shows 97% likelihood of NPV\>£2bn and 71% likelihood of NPV\>£3bn over 20 years. Recovery of total lifetime costs (to 2045) is expected within the first year of partial rollout (2030).
Pre-Innovation Baseline
Currently, DSO and NESO actions only partially coordinate. Limited real-time data sharing and fragmented coordination preserve system security but reduce operational efficiency. Conservative, siloed constraint management increases risk of:
Duplicated/counteracting cross-network actions.
Inefficient flexibility deployment.
Avoidable renewable curtailment.
Inefficiencies drive high balancing costs (£2.7bn in 2024/25, NESO 2030 forecast of £8bn). Current flexibility inefficiency and cross-network conflicts are low but expected to worsen as flexibility markets develop and Distributed Energy Resource (DER) penetration rises towards 30-35% of generation by 2050 (FES2025).
Baseline metrics include Balancing Mechanism (BM) costs, constraint volumes, flexibility dispatch costs, curtailment levels, and carbon intensity.
Project Benefits
Coordinating primacy, Active Network Management, and BM signals, reducing conflict.
Integrating DNO and NESO forecasts improving short-term demand and flexibility predictions.
Providing distribution-level analytics for NESO's Volta programme.
Demonstrating value from data sharing via ICCP.
Benefit Mechanisms
FF reduces unnecessary/inefficient operational actions. Benefits arise from increasing efficiency, achieving the same outcomes at lower cost. Mechanisms include:
Avoiding counteracting transmission and distribution actions, lowering constraint volumes.
Better-targeted flexibility dispatch.
Improved confidence and forecasting in DER availability, reducing conservativism and renewable curtailment, reducing high-cost BM actions.
The effects are quantified in the CBA, using referenced BM and flexibility market data/projections and NESO estimates of improved cross-network visibility. Uncertainty is assessed via MC simulation, with conservatism built into assumptions.
Future Reductions in the Cost of Operating the Network: First-centile - £1.8bn (2026-2045)
FF reduces network operator costs. The absolute minimum forecast NPV is £689m by 2045, with a median of £3.5bn. The minimum savings from 2026-2045 independently arise through:
Reduced constraint actions: £344m (primarily NESO savings).
Better flexibility efficiency: £342m (primarily DSO savings).
Improved forecasts: £19m (NESO and DSO savings).
Network savings are assessed at whole-system level, noting all DSOs and NESO are Beta partners.
Carbon Reduction -- Indirect CO2 Savings per Annum: First-centile - 92ktCO2 (2026-2045)
FF reduces constraint management and renewable curtailment, lowering carbon emissions from fossil-fuel replacement generation. The forecast reduction absolute minimum is 37ktCO2 by 2045 and median 209ktCO2, or ~14ktCO2 saving per year of operation.
Cost Savings per Annum on Energy Bills for Consumers: Unquantified
By reducing whole-system operating costs, FF will deliver pass-through savings to consumers. For example, lower BM costs reduce Balancing Services Use of System (BSUoS) charges, wholly recovered from generators and suppliers and passed to consumers. Beta focuses on quantifying total consumer savings.
Improved Access to Revenues for Users of Network Services: Unquantified
FF will enable new revenue streams for service users and future service development, including:
Improved access to flexibility service revenues through clearer operational signals and predictable market participation,
System inertia insights through assessing embedded asset contributions.
Summary
FF is expected to deliver significant, low‑risk net benefits through network cost savings and indirect carbon reduction, leading to lower consumer bills. Additional benefits include improved revenue access for flexibility service users.
