Identify and quantify the system states that create risk—without exhaustively simulating every possible contingency.
(Annual Risk)
(Per Element)
(System Impact)
(Probability of Overload)
THE PROBLEM
Weather, loads, outages, topology, and sensor data constantly shift—driving endless re-analysis.
AC power flow, outages, thermal behavior, CFD, and large scenario spaces create a combinatorial explosion.
HOW IT WORKS
Model uncertainty across drivers, correlate scenarios, and reduce the space to what drives scenario representation.
Use intelligent filtering to choose the minimal set of high-fidelity simulations that maximizes information.
Aggregate simulation results to produce probabilistic risk metrics with quantified confidence.
INDUSTRIES WE SERVE
Helping utilities and operators make better risk-informed decisions for a reliable grid.
Quantify risk to critical facility power and cooling under uncertain conditions.
WHAT WE MEASURE (PROOF)
Every number above is computed from committed
benchmark results in our study repository by
website/metrics/generate_metrics.py; the study named on
each card is the file it comes from. Reference systems are RTS-GMLC
and congested replicas of it.
The coverage advantage is largest on congested systems with many probable critical states, and modest on an uncongested base case (0.96 against 0.92 for the best baseline on RTS-GMLC itself). Our method buys completeness and a certified bound, not raw speed: in these comparisons it spends more wall-clock than Monte Carlo to do it.
WHY TERRABYTE
Terrabyte Analytics builds probabilistic physical-risk analysis for networked engineered systems, combining system physics, scenario intelligence, and selective high-fidelity simulation. Monte Carlo tells you what it happened to draw. We tell you what you cannot afford to miss, and prove the bound.
Questions about the method, the benchmarks, or whether any of it applies to your system.