VolteGrid™ predicts, quantifies and helps prevent cascading failures in DER dominated networks in real time. It fuses live telemetry with physics-informed stochastic models and a graph neural network cascade surrogate, resolved into a continuously updated reliability index.
Live operations cockpit below ↓Real-time cascade intelligence for DER dominated distribution networks.
VolteGrid™ is an AI-native grid intelligence platform that predicts, quantifies and helps prevent cascading failures in DER dominated power systems, in real time. It fuses live network telemetry with physics-informed stochastic modelling and a graph neural network cascade surrogate, delivering a continuously updated index for every operational decision.
A live view of the VolteGrid™ operator console. Network status shifts as local disturbances develop and clear across the feeder group.
Distribution networks were engineered for one-way power flow, predictable load and abundant rotational inertia. That grid is gone. Rooftop solar, batteries, EVs and heat pumps have turned the LV and MV network into a fast, bidirectional, low-inertia system, where disturbances propagate in ways legacy protection and planning tools were never designed to see.
Inverter-based resources respond in milliseconds and mass-disconnect on frequency and voltage excursions.
A feeder trip, a protection maloperation or a correlated DER disconnection propagates through electrical, protection and control couplings into system-scale loss.
SAIDI and SAIFI describe the past. They say nothing about the probability, geometry or severity of the next failure sequence.
VolteGrid™ ingests the network as it actually is: topology, live measurements, DER behaviour, protection configuration. It answers three questions continuously.
Stochastic point-process models, running in real time, learn the self-exciting structure of network events: how one disturbance raises the probability of the next, where, and on what timescale. This captures the clustered, bursty character of real failure data that classical independent-failure models miss.
A graph neural network cascade surrogate, trained against high-fidelity physical simulation, evaluates propagation across the network graph orders of magnitude faster than direct simulation: fast enough to score thousands of contingency and disturbance scenarios inside the operational decision window.
The outputs resolve into a single multi-dimensional reliability index: a live measure of network health that decomposes across time, location, asset class and failure mode. Designed to sit alongside, and ultimately supersede, the scalar averages the industry has relied on for forty years.
Every AI component is anchored to network physics and validated against full physical simulation. VolteGrid™ is not a black box scoring engine. It is a physically grounded inference system with machine learning used precisely where it buys speed and coverage.
Models tuned on synthetic benchmarks routinely fail on the messiness of live networks: missing telemetry, asymmetric feeders, correlated weather-driven events. VolteGrid™ was built the other way round: from real network data outward, using openly published operational datasets from Great Britain's network operators.
Real distribution topologies, event records and DER penetration profiles across London, the South East and East of England.
System-level operational datasets grounding the transmission-distribution interface and frequency-event context.
Network and outage datasets extending validation coverage across distinct geography, weather exposure and network construction.
VolteGrid™ originates from research in Engineering Science at the University of Oxford on cascading failure dynamics and reliability engineering for DER dominated distribution grids. That research lineage runs through the product: the stochastic modelling framework and the cascade surrogate methodology are the direct descendants of peer-oriented academic work, hardened into an operational platform.
VolteGrid™ is working with forward-leaning network operators to deploy live cascade intelligence on real networks.