Publications

The science behind Gridraven's technology

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IEEE PES GM Best Paper Session

Operational Integration of Dynamic Line Rating Using IEC 60870-5-104 and CIM/CGMES

How to put Dynamic Line Rating into day-to-day control-centre operation using IEC 60870-5-104 and CIM/CGMES, not a parallel pilot stack or custom data exchange formats. Key idea: probabilistic, span-aware DLR → line-level Secure DLR (98% CI) → familiar channels operators already trust. Validated on the Estonian transmission grid. Presented at the IEEE PES GM Best Paper Session. The paper is awaiting publication in IEEE Xplore.

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IEEE

Span-Level Dynamic Line Rating from Machine-Learned Wind Downscaling

Validation against line-mounted sensors. The rating falls below the nominal 95% bound for 5.1% of hours and the 99% bound for 0.6%. Out of 116 spans, 75 are limiting at least in some hour and the binding span changes between consecutive hours 39% of the time.

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A case study covering 5000 km of lines

Dynamic Line Rating using Hyper-local Weather Predictions

A Case Study Covering 5000 km Estonian Transmission Lines. This paper presents a large-scale implementation of a sensorless Dynamic Line Rating (DLR) system across the Estonian high-voltage transmission grid. The results confirm that a sensorless DLR system using machine learning and high-resolution weather data can scale to national grid levels.

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Report

Grid Congestion Analysis for PJM. Impact of Dynamic Line Rating on Market Constraints

In this report, the financial impact of transmission constraints within the PJM Interconnection is analyzed, focusing on how Gridraven’s machine learning-based Dynamic Line Rating (DLR) technology can mitigate these bottlenecks.

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Boosting the grid in Texas

Economic impact of near-term options for reducing congestion

Congestion costs in ERCOT have been between $1 to $2 billion each year. The study looks at the economic benefit from two short-term options for reducing congestion: (1) Dynamic Line Ratings and (2) treating the San Miguel - Elm Creek double circuit 345 kV line as a double contingency.

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Texas validation

Wind forecast accuracy for DLR in Texas

In this report, the Gridraven machine learning based wind speed predictions and large-scale Numerical Weather Prediction (NWP) wind speeds are compared against physical measurements from meteorological stations across the state of Texas.

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IEEE

Dynamic Line Rating using Hyper-local Weather Predictions

A description of Gridraven's weather downscaling technology. Gridraven Claw is based on a machine learning approach for hyper-local weather prediction using digital elevation models from airborne lidar scanning and satellite earth observation.

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