Aug 2013
Electricity Transmission
Enhanced Weather Modeling for Dynamic Line rating (DLR)
Aug 2013
Aug 2016
National Grid Electricity System Operator, National Grid Electricity Transmission and SP Energy Networks
Anna Blackwell
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Network Innovation Allowance
Low Carbon Generation
This project aims to establish the spare thermal capacity in overhead lines that exists as a result of the actual weather parameters compared to seasonal values, and forecast the capacity that will be available ahead of real-time. As such, operational decisions will be able to be made which will reduce the cost of operating the system and potentially avoid or defer reinforcement works following the connection of new low carbon generation.

The project is being undertaken in partnership with Scottish Power Energy Network (SPEN), the University of Strathclyde and the Scottish Energy Technology Partnership (ETP) and builds on the IET Innovation Award-winning work undertaken with SPEN at the University of Durham and the LCNF tier 1 project (SPT1001).

The scope of the project is to explore how Dynamic Line Rating (DLR) can be more widely exploited within various timescales, utilising historic weather data to estimate prevailing capacity along overhead line routes to establish:

  • The suitability of applying to a variety of short term capacities,

  • How to utilize DLR in longer timescales to make investment or operational planning decisions such as reinforcement works or contracting strategy,

  • Investigate suitable risk factors to apply to forecast ratings at different timescales based on predictability of the weather forecasts ahead of real time.

The objectives for the project include:

  • establish the level of confidence that can be attributed to low cost overhead line rating forecasting techniques that predict overhead line ratings sufficiently far ahead of real time to be useful for operational planning decisions.
The following success criteria have been established:

  • Complete a comparison of the dynamic ratings methods that are described in published engineering literature and summarising the different features that are apparent from the literature.

  • Develop a new statistical forecasting model that addresses wind speed, wind direction and temperature, quantifying the probabilities of particular forecast quantities being exceeded.

  • Evaluate the use of enhanced dynamic ratings in particular GB power system contexts.